diff --git a/flatland/evaluators/service.py b/flatland/evaluators/service.py
index 7eef7f0667335cddd233a17327ea06960e8cfc97..7ca6f39a1cea2ad89ec5daf19f1900b3685440be 100644
--- a/flatland/evaluators/service.py
+++ b/flatland/evaluators/service.py
@@ -48,18 +48,40 @@ m.patch()
########################################################
# CONSTANTS
########################################################
+
+# Don't proceed to next Test if the previous one
+# didn't reach this mean completion percentage
+TEST_MIN_PERCENTAGE_COMPLETE_MEAN = 0.25
+
+# After this number of consecutive timeouts, kill the submission:
+# this probably means the submission has crashed
+MAX_SUCCESSIVE_TIMEOUTS = 10
+
+debug_mode = (os.getenv("AICROWD_DEBUG_SUBMISSION", 0) == 1)
+if debug_mode:
+ print("=" * 20)
+ print("Submission in DEBUG MODE! will get limited time")
+ print("=" * 20)
+
+# 8 hours (will get debug timeout from env variable if applicable)
+OVERALL_TIMEOUT = int(os.getenv(
+ "FLATLAND_OVERALL_TIMEOUT",
+ 8 * 60 * 60))
+
+# 10 mins
INTIAL_PLANNING_TIMEOUT = int(os.getenv(
"FLATLAND_INITIAL_PLANNING_TIMEOUT",
- 5 * 60)) # 5 mins
+ 10 * 60))
+
+# 10 seconds
PER_STEP_TIMEOUT = int(os.getenv(
"FLATLAND_PER_STEP_TIMEOUT",
- 5)) # 5 seconds
+ 10))
+
+# 5 min - applies to the rest of the commands
DEFAULT_COMMAND_TIMEOUT = int(os.getenv(
"FLATLAND_DEFAULT_COMMAND_TIMEOUT",
- 1 * 60)) # 1 min
-# This applies to the rest of the commands
-
-MAX_SUCCESSIVE_TIMEOUTS = 10
+ 5 * 60))
RANDOM_SEED = int(os.getenv("FLATLAND_EVALUATION_RANDOM_SEED", 1001))
@@ -90,56 +112,58 @@ class FlatlandRemoteEvaluationService:
numpy arrays).
"""
- def __init__(self,
- test_env_folder="/tmp",
- flatland_rl_service_id='FLATLAND_RL_SERVICE_ID',
- remote_host='127.0.0.1',
- remote_port=6379,
- remote_db=0,
- remote_password=None,
- visualize=False,
- video_generation_envs=[],
- report=None,
- verbose=False,
- actionDir=None,
- episodeDir=None,
- mergeDir=None,
- use_pickle=False,
- shuffle=True,
- missing_only=False,
- result_output_path=None,
- disable_timeouts=False
- ):
+ def __init__(
+ self,
+ test_env_folder="/tmp",
+ flatland_rl_service_id='FLATLAND_RL_SERVICE_ID',
+ remote_host='127.0.0.1',
+ remote_port=6379,
+ remote_db=0,
+ remote_password=None,
+ visualize=False,
+ video_generation_envs=[],
+ report=None,
+ verbose=False,
+ action_dir=None,
+ episode_dir=None,
+ merge_dir=None,
+ use_pickle=False,
+ shuffle=False,
+ missing_only=False,
+ result_output_path=None,
+ disable_timeouts=False
+ ):
# Episode recording properties
- self.actionDir = actionDir
- if actionDir and not os.path.exists(self.actionDir):
- os.makedirs(self.actionDir)
- self.episodeDir = episodeDir
- if episodeDir and not os.path.exists(self.episodeDir):
- os.makedirs(self.episodeDir)
- self.mergeDir = mergeDir
- if mergeDir and not os.path.exists(self.mergeDir):
- os.makedirs(self.mergeDir)
+ self.action_dir = action_dir
+ if action_dir and not os.path.exists(self.action_dir):
+ os.makedirs(self.action_dir)
+ self.episode_dir = episode_dir
+ if episode_dir and not os.path.exists(self.episode_dir):
+ os.makedirs(self.episode_dir)
+ self.merge_dir = merge_dir
+ if merge_dir and not os.path.exists(self.merge_dir):
+ os.makedirs(self.merge_dir)
self.use_pickle = use_pickle
self.missing_only = missing_only
- self.disable_timeouts = disable_timeouts
+ self.episode_actions = []
+ self.disable_timeouts = disable_timeouts
if self.disable_timeouts:
print("=" * 20)
print("Timeout are DISABLED!")
print("=" * 20)
- if not shuffle:
+ if shuffle:
print("=" * 20)
- print("Env shuffling is DISABLED!")
+ print("Env shuffling is ENABLED! not suitable for infinite wave")
print("=" * 20)
# Test Env folder Paths
self.test_env_folder = test_env_folder
self.video_generation_envs = video_generation_envs
self.env_file_paths = self.get_env_filepaths()
-
+ print(self.env_file_paths)
# Shuffle all the env_file_paths for more exciting videos
# and for more uniform time progression
if shuffle:
@@ -153,13 +177,13 @@ class FlatlandRemoteEvaluationService:
self.report = report
# Use a state to swallow and ignore any steps after an env times out.
- # this should be reset to False after env reset() to get the next env.
self.state_env_timed_out = False
# Count the number of successive timeouts (will kill after MAX_SUCCESSIVE_TIMEOUTS)
# This prevents a crashed submission to keep running forever
self.timeout_counter = 0
+ # Results are the metrics: percent done, rewards, timing...
self.result_output_path = result_output_path
# Communication Protocol Related vars
@@ -169,7 +193,6 @@ class FlatlandRemoteEvaluationService:
self.namespace,
self.service_id
)
-
self.error_channel = "{}::{}::errors".format(
self.namespace,
self.service_id
@@ -212,12 +235,18 @@ class FlatlandRemoteEvaluationService:
self.simulation_rewards = []
self.simulation_rewards_normalized = []
self.simulation_percentage_complete = []
+ self.simulation_percentage_complete_per_test = {}
self.simulation_steps = []
self.simulation_times = []
self.env_step_times = []
self.nb_malfunctioning_trains = []
+ self.overall_start_time = 0
+ self.termination_cause = "No reported termination cause."
+ self.evaluation_done = False
self.begin_simulation = False
self.current_step = 0
+ self.current_test = -1
+ self.current_level = -1
self.visualize = visualize
self.vizualization_folder_name = "./.visualizations"
self.record_frame_step = 0
@@ -232,7 +261,7 @@ class FlatlandRemoteEvaluationService:
def update_running_stats(self, key, scalar):
"""
- Computes the running mean for certain params
+ Computes the running min/mean/max for given param
"""
mean_key = "{}_mean".format(key)
counter_key = "{}_counter".format(key)
@@ -293,40 +322,47 @@ class FlatlandRemoteEvaluationService:
  ├── .......
└── Level_99.pkl
"""
- env_paths = sorted(
- glob.glob(
- os.path.join(
- self.test_env_folder,
- "*/*.pkl"
- )
+ env_paths = glob.glob(
+ os.path.join(
+ self.test_env_folder,
+ "*/*.pkl"
)
)
# Remove the root folder name from the individual
# lists, so that we only have the path relative
# to the test root folder
- env_paths = sorted([os.path.relpath(
- x, self.test_env_folder
- ) for x in env_paths])
-
- # Sort in proper order
- def get_file_order(f):
- numbers = re.findall(r'\d+', os.path.relpath(f))
- value = int(numbers[0]) * 1000 + int(numbers[1])
+ env_paths = [os.path.relpath(x, self.test_env_folder) for x in env_paths]
+
+ # Sort in proper numerical order
+ def get_file_order(filename):
+ test_id, level_id = self.get_env_test_and_level(filename)
+ value = test_id * 1000 + level_id
return value
env_paths.sort(key=get_file_order)
# if requested, only generate actions for those envs which don't already have them
- if self.mergeDir and self.missing_only:
+ if self.merge_dir and self.missing_only:
existing_paths = (itertools.chain.from_iterable(
- [glob.glob(os.path.join(self.mergeDir, f"envs/*.{ext}"))
+ [glob.glob(os.path.join(self.merge_dir, f"envs/*.{ext}"))
for ext in ["pkl", "mpk"]]))
- existing_paths = [os.path.relpath(sPath, self.mergeDir) for sPath in existing_paths]
+ existing_paths = [os.path.relpath(sPath, self.merge_dir) for sPath in existing_paths]
env_paths = set(env_paths) - set(existing_paths)
return env_paths
+ def get_env_test_and_level(self, filename):
+ numbers = re.findall(r'\d+', os.path.relpath(filename))
+
+ if len(numbers) == 2:
+ test_id = int(numbers[0])
+ level_id = int(numbers[1])
+ else:
+ print(numbers)
+ raise ValueError("Unexpected file path, expects 'Test_<N>/Level_<M>.pkl', found", filename)
+ return test_id, level_id
+
def instantiate_evaluation_metadata(self):
"""
This instantiates a pandas dataframe to record
@@ -587,10 +623,10 @@ class FlatlandRemoteEvaluationService:
def handle_env_create(self, command):
"""
Handles a ENV_CREATE command from the client
- TODO: Add a high level summary of everything thats happening here.
"""
- if not self.simulation_done:
- # trying to reset a simulation before finishing the previous one
+
+ # Check if the previous episode was finished
+ if not self.simulation_done and not self.evaluation_done:
_command_response = self._error_template("CAN'T CREATE NEW ENV BEFORE PREVIOUS IS DONE")
self.send_response(_command_response, command)
raise Exception(_command_response['payload'])
@@ -598,19 +634,50 @@ class FlatlandRemoteEvaluationService:
self.simulation_count += 1
self.simulation_done = False
+ if self.simulation_count == 0:
+ # Very first episode: start the overall timer
+ print("Starting overall timer...")
+ self.overall_start_time = time.time()
+
# reset the timeout flag / state.
self.state_env_timed_out = False
- if self.simulation_count < len(self.env_file_paths):
+ test_env_file_path = self.env_file_paths[self.simulation_count]
+ env_test, env_level = self.get_env_test_and_level(test_env_file_path)
+
+ # Did we just finish a test, and if yes did it reach high enough mean percentage done?
+ if self.current_test != env_test and env_test != 0:
+ if self.current_test not in self.simulation_percentage_complete_per_test:
+ print("No environment was finished at all during test {}!".format(self.current_test))
+ mean_test_complete_percentage = 0.0
+ else:
+ mean_test_complete_percentage = np.mean(self.simulation_percentage_complete_per_test[self.current_test])
+
+ if mean_test_complete_percentage < TEST_MIN_PERCENTAGE_COMPLETE_MEAN:
+ print("=" * 15)
+ msg = "The mean percentage of done agents during the last 10 environments was too low: {:.3f} < {}".format(
+ mean_test_complete_percentage,
+ TEST_MIN_PERCENTAGE_COMPLETE_MEAN
+ )
+ print(msg, "Evaluation will stop.")
+ self.termination_cause = msg
+ self.evaluation_done = True
+
+ if self.simulation_count < len(self.env_file_paths) and not self.evaluation_done:
"""
There are still test envs left that are yet to be evaluated
"""
- test_env_file_path = self.env_file_paths[self.simulation_count]
+
print("Evaluating {} ({}/{})".format(test_env_file_path, self.simulation_count, len(self.env_file_paths)))
+
test_env_file_path = os.path.join(
self.test_env_folder,
test_env_file_path
)
+
+ self.current_test = env_test
+ self.current_level = env_level
+
del self.env
self.env = RailEnv(width=1, height=1,
rail_generator=rail_from_file(test_env_file_path),
@@ -634,6 +701,7 @@ class FlatlandRemoteEvaluationService:
self.simulation_rewards.append(0)
self.simulation_rewards_normalized.append(0)
self.simulation_percentage_complete.append(0)
+ self.simulation_times.append(0)
self.simulation_steps.append(0)
self.nb_malfunctioning_trains.append(0)
@@ -676,20 +744,23 @@ class FlatlandRemoteEvaluationService:
#####################################################################
# Update evaluation state
#####################################################################
+ elapsed = time.time() - self.overall_start_time
progress = np.clip(
- self.simulation_count * 1.0 / len(self.env_file_paths),
+ elapsed / OVERALL_TIMEOUT,
0, 1)
- mean_reward, mean_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
+ mean_reward, mean_normalized_reward, sum_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
self.evaluation_state["state"] = "IN_PROGRESS"
self.evaluation_state["progress"] = progress
self.evaluation_state["simulation_count"] = self.simulation_count
- self.evaluation_state["score"]["score"] = mean_percentage_complete
- self.evaluation_state["score"]["score_secondary"] = mean_reward
+ self.evaluation_state["score"]["score"] = sum_normalized_reward
+ self.evaluation_state["score"]["score_secondary"] = mean_percentage_complete
self.evaluation_state["meta"]["normalized_reward"] = mean_normalized_reward
+ self.evaluation_state["meta"]["termination_cause"] = self.termination_cause
self.handle_aicrowd_info_event(self.evaluation_state)
- self.lActions = []
+
+ self.episode_actions = []
def handle_env_step(self, command):
"""
@@ -697,20 +768,34 @@ class FlatlandRemoteEvaluationService:
TODO: Add a high level summary of everything thats happening here.
"""
- if self.state_env_timed_out:
- print("Ignoring step command after timeout")
+ if self.state_env_timed_out or self.evaluation_done:
+ print("Ignoring step command after timeout.")
return
_payload = command['payload']
if not self.env:
- raise Exception(
- "env_client.step called before env_client.env_create() call")
+ raise Exception("env_client.step called before env_client.env_create() call")
if self.env.dones['__all__']:
raise Exception(
"Client attempted to perform an action on an Env which \
has done['__all__']==True")
+ overall_elapsed = (time.time() - self.overall_start_time)
+ if overall_elapsed > OVERALL_TIMEOUT:
+ msg = "Reached overall time limit: took {:.2f}s, limit is {:.2f}s.".format(
+ overall_elapsed, OVERALL_TIMEOUT
+ )
+ self.termination_cause = msg
+ self.evaluation_done = True
+
+ print("=" * 15)
+ print(msg, "Evaluation will stop.")
+ return
+ # else:
+ # print("="*15)
+ # print("{}s left!".format(OVERALL_TIMEOUT - overall_elapsed))
+
action = _payload['action']
inference_time = _payload['inference_time']
# We record this metric in two keys:
@@ -719,6 +804,7 @@ class FlatlandRemoteEvaluationService:
self.update_running_stats("current_episode_controller_inference_time", inference_time)
self.update_running_stats("controller_inference_time", inference_time)
+ # Perform the step
time_start = time.time()
_observation, all_rewards, done, info = self.env.step(action)
time_diff = time.time() - time_start
@@ -736,29 +822,31 @@ class FlatlandRemoteEvaluationService:
that episode
"""
self.simulation_rewards_normalized[-1] += \
- cumulative_reward / (
+ (cumulative_reward / (
self.env._max_episode_steps *
self.env.get_num_agents()
- )
+ ))
- num_malfunctioning = sum(agent.malfunction_data['malfunction'] > 0 for agent in self.env.agents)
- if (num_malfunctioning > 0):
- print(num_malfunctioning, "agent malfunctioning at step", self.current_step)
+ # We count the number of agents that malfunctioned by checking how many have 1 more steps left before recovery
+ num_malfunctioning = sum(agent.malfunction_data['malfunction'] == 1 for agent in self.env.agents)
+
+ if self.verbose and num_malfunctioning > 0:
+ print("Step {}: {} agents have malfunctioned and will recover next step".format(self.current_step, num_malfunctioning))
self.nb_malfunctioning_trains[-1] += num_malfunctioning
# record the actions before checking for done
- if self.actionDir is not None:
- self.lActions.append(action)
+ if self.action_dir is not None:
+ self.episode_actions.append(action)
- # all done! episode over
+ # Is the episode over?
if done["__all__"]:
self.simulation_done = True
if self.begin_simulation:
# If begin simulation has already been initialized at least once
# This adds the simulation time for the previous episode
- self.simulation_times.append(time.time() - self.begin_simulation)
+ self.simulation_times[-1] = time.time() - self.begin_simulation
# Compute percentage complete
complete = 0
@@ -769,26 +857,38 @@ class FlatlandRemoteEvaluationService:
percentage_complete = complete * 1.0 / self.env.get_num_agents()
self.simulation_percentage_complete[-1] = percentage_complete
- print("Evaluation finished in {} timesteps, {:.3f} seconds. Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}.".format(
- self.simulation_steps[-1],
- self.simulation_times[-1],
- self.simulation_percentage_complete[-1],
- self.simulation_rewards_normalized[-1],
- self.nb_malfunctioning_trains[-1]
- ))
+ # adds 1.0 so we can add them up
+ self.simulation_rewards_normalized[-1] += 1.0
+
+ if self.current_test not in self.simulation_percentage_complete_per_test:
+ self.simulation_percentage_complete_per_test[self.current_test] = []
+ self.simulation_percentage_complete_per_test[self.current_test].append(percentage_complete)
+ print("Percentage for test {}, level {}: {}".format(self.current_test, self.current_level, percentage_complete))
+ print(self.simulation_percentage_complete_per_test[self.current_test])
+
+ print(
+ "Evaluation finished in {} timesteps, {:.3f} seconds. Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}.".format(
+ self.simulation_steps[-1],
+ self.simulation_times[-1],
+ self.simulation_percentage_complete[-1],
+ self.simulation_rewards_normalized[-1],
+ self.nb_malfunctioning_trains[-1]
+ ))
+
+ print("Total normalized reward so far: {:.3f}".format(sum(self.simulation_rewards_normalized)))
# Write intermediate results
if self.result_output_path:
self.evaluation_metadata_df.to_csv(self.result_output_path)
print("Wrote intermediate output results to : {}".format(self.result_output_path))
- if self.actionDir is not None:
+ if self.action_dir is not None:
self.save_actions()
- if self.episodeDir is not None:
+ if self.episode_dir is not None:
self.save_episode()
- if self.mergeDir is not None:
+ if self.merge_dir is not None:
self.save_merged_env()
# Record Frame
@@ -816,7 +916,7 @@ class FlatlandRemoteEvaluationService:
def save_actions(self):
sfEnv = self.env_file_paths[self.simulation_count]
- sfActions = self.actionDir + "/" + sfEnv.replace(".pkl", ".json")
+ sfActions = self.action_dir + "/" + sfEnv.replace(".pkl", ".json")
print("env path: ", sfEnv, " sfActions:", sfActions)
@@ -824,20 +924,20 @@ class FlatlandRemoteEvaluationService:
os.makedirs(os.path.dirname(sfActions))
with open(sfActions, "w") as fOut:
- json.dump(self.lActions, fOut)
+ json.dump(self.episode_actions, fOut)
- self.lActions = []
+ self.episode_actions = []
def save_episode(self):
sfEnv = self.env_file_paths[self.simulation_count]
- sfEpisode = self.episodeDir + "/" + sfEnv
+ sfEpisode = self.episode_dir + "/" + sfEnv
print("env path: ", sfEnv, " sfEpisode:", sfEpisode)
RailEnvPersister.save_episode(self.env, sfEpisode)
# self.env.save_episode(sfEpisode)
def save_merged_env(self):
sfEnv = self.env_file_paths[self.simulation_count]
- sfMergeEnv = self.mergeDir + "/" + sfEnv
+ sfMergeEnv = self.merge_dir + "/" + sfEnv
if not os.path.exists(os.path.dirname(sfMergeEnv)):
os.makedirs(os.path.dirname(sfMergeEnv))
@@ -878,14 +978,14 @@ class FlatlandRemoteEvaluationService:
# Compute the evaluation metadata for the last episode
self.update_evaluation_metadata()
- if len(self.simulation_rewards) != len(self.env_file_paths):
+ if len(self.simulation_rewards) != len(self.env_file_paths) and not self.evaluation_done:
raise Exception(
"""env.submit called before the agent had the chance
to operate on all the test environments.
"""
)
- mean_reward, mean_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
+ mean_reward, mean_normalized_reward, sum_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
if self.visualize and len(os.listdir(self.vizualization_folder_name)) > 0:
# Generate the video
@@ -946,24 +1046,30 @@ class FlatlandRemoteEvaluationService:
#####################################################################
# Update evaluation state
#####################################################################
+
self.evaluation_state["state"] = "FINISHED"
self.evaluation_state["progress"] = 1.0
self.evaluation_state["simulation_count"] = self.simulation_count
- self.evaluation_state["score"]["score"] = mean_percentage_complete
- self.evaluation_state["score"]["score_secondary"] = mean_reward
+ self.evaluation_state["score"]["score"] = sum_normalized_reward
+ self.evaluation_state["score"]["score_secondary"] = mean_percentage_complete
self.evaluation_state["meta"]["normalized_reward"] = mean_normalized_reward
self.evaluation_state["meta"]["reward"] = mean_reward
self.evaluation_state["meta"]["percentage_complete"] = mean_percentage_complete
+ self.evaluation_state["meta"]["termination_cause"] = self.termination_cause
self.handle_aicrowd_success_event(self.evaluation_state)
+
print("#" * 100)
print("EVALUATION COMPLETE !!")
print("#" * 100)
print("# Mean Reward : {}".format(mean_reward))
+ print("# Sum Normalized Reward : {} (primary score)".format(sum_normalized_reward))
+ print("# Mean Percentage Complete : {} (secondary score)".format(mean_percentage_complete))
print("# Mean Normalized Reward : {}".format(mean_normalized_reward))
- print("# Mean Percentage Complete : {}".format(mean_percentage_complete))
print("#" * 100)
print("#" * 100)
+ return _command_response
+
def compute_mean_scores(self):
#################################################################################
#################################################################################
@@ -971,22 +1077,21 @@ class FlatlandRemoteEvaluationService:
# we group all the results by the test_ids
# so we first compute the mean in each of the test_id groups,
# and then we compute the mean across each of the test_id groups
- #
- #
#################################################################################
#################################################################################
source_df = self.evaluation_metadata_df.dropna()
- grouped_df = source_df.groupby(['test_id']).mean()
+ # grouped_df = source_df.groupby(['test_id']).mean()
- mean_reward = grouped_df["reward"].mean()
- mean_normalized_reward = grouped_df["normalized_reward"].mean()
- mean_percentage_complete = grouped_df["percentage_complete"].mean()
+ mean_reward = source_df["reward"].mean()
+ mean_normalized_reward = source_df["normalized_reward"].mean()
+ sum_normalized_reward = source_df["normalized_reward"].sum()
+ mean_percentage_complete = source_df["percentage_complete"].mean()
# Round off the reward values
mean_reward = round(mean_reward, 2)
mean_normalized_reward = round(mean_normalized_reward, 5)
mean_percentage_complete = round(mean_percentage_complete, 3)
- return mean_reward, mean_normalized_reward, mean_percentage_complete
+ return mean_reward, mean_normalized_reward, sum_normalized_reward, mean_percentage_complete
def report_error(self, error_message, command_response_channel):
"""
@@ -1005,6 +1110,7 @@ class FlatlandRemoteEvaluationService:
)
self.evaluation_state["state"] = "ERROR"
self.evaluation_state["error"] = error_message
+ self.evaluation_state["meta"]["termination_cause"] = "An error occured."
self.handle_aicrowd_error_event(self.evaluation_state)
def handle_aicrowd_info_event(self, payload):
@@ -1032,37 +1138,45 @@ class FlatlandRemoteEvaluationService:
"""
print("Listening at : ", self.command_channel)
MESSAGE_QUEUE_LATENCY = []
- while True:
+ while True:
try:
command = self.get_next_command()
except timeout_decorator.timeout_decorator.TimeoutError:
# a timeout occurred: send an error, and give -1.0 normalized score for this episode
if self.previous_command['type'] == messages.FLATLAND_RL.ENV_STEP:
self.send_error({"type": messages.FLATLAND_RL.ENV_STEP_TIMEOUT})
+ timeout_details = "step time limit of {}s".format(PER_STEP_TIMEOUT)
elif self.previous_command['type'] == messages.FLATLAND_RL.ENV_CREATE:
self.send_error({"type": messages.FLATLAND_RL.ENV_RESET_TIMEOUT})
+ timeout_details = "pre-planning time limit of {}s".format(INTIAL_PLANNING_TIMEOUT)
self.simulation_steps[-1] += 1
self.simulation_rewards[-1] = self.env._max_episode_steps * self.env.get_num_agents()
- self.simulation_rewards_normalized[-1] = -1.0
-
- print("Evaluation TIMED OUT after {} timesteps, using max penalty. Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}".format(
- self.simulation_steps[-1],
- self.simulation_percentage_complete[-1],
- self.simulation_rewards_normalized[-1],
- self.nb_malfunctioning_trains[-1],
- ))
+ self.simulation_rewards_normalized[-1] = 0.0
+
+ print(
+ "Evaluation of this episode TIMED OUT after {} timesteps (exceeded {}), won't get any reward. {} consecutive timeouts. "
+ "Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}.".format(
+ self.simulation_steps[-1],
+ timeout_details,
+ self.timeout_counter,
+ self.simulation_percentage_complete[-1],
+ self.simulation_rewards_normalized[-1],
+ self.nb_malfunctioning_trains[-1],
+ ))
self.timeout_counter += 1
self.state_env_timed_out = True
self.simulation_done = True
- print("Consecutive timeouts: {}".format(self.timeout_counter))
- if self.timeout_counter > MAX_SUCCESSIVE_TIMEOUTS:
- raise Exception("{} consecutive timeouts, aborting.".format(self.timeout_counter))
-
+ if self.timeout_counter >= MAX_SUCCESSIVE_TIMEOUTS:
+ print("=" * 15)
+ msg = "Submissions had {} consecutive timeouts.".format(self.timeout_counter)
+ print(msg, "Evaluation will stop.")
+ self.termination_cause = msg
+ self.evaluation_done = True
continue
self.timeout_counter = 0
@@ -1180,10 +1294,10 @@ if __name__ == "__main__":
help="use pickle instead of msgpack",
required=False)
- parser.add_argument('--noShuffle',
+ parser.add_argument('--shuffle',
default=False,
action="store_true",
- help="don't shuffle the envs. Default is to shuffle.",
+ help="Shuffle the environments",
required=False)
parser.add_argument('--disableTimeouts',
@@ -1203,7 +1317,6 @@ if __name__ == "__main__":
help="Results CSV path",
required=False)
-
parser.add_argument('--verbose',
default=False,
action="store_true",
@@ -1220,11 +1333,11 @@ if __name__ == "__main__":
visualize=True,
video_generation_envs=["Test_0/Level_100.pkl"],
result_output_path=args.resultsDir,
- actionDir=args.actionDir,
- episodeDir=args.episodeDir,
- mergeDir=args.mergeDir,
+ action_dir=args.actionDir,
+ episode_dir=args.episodeDir,
+ merge_dir=args.mergeDir,
use_pickle=args.pickle,
- shuffle=not args.noShuffle,
+ shuffle=args.shuffle,
missing_only=args.missingOnly,
disable_timeouts=args.disableTimeouts
)
diff --git a/notebooks/test-service.ipynb b/notebooks/test-service.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..969e8420629f05a562784cb3b9d36e83bfb98838
--- /dev/null
+++ b/notebooks/test-service.ipynb
@@ -0,0 +1,2158 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Test Service\n",
+ "\n",
+ "Intended to test the service.py evaluator.\n",
+ "Runs the service.py and a simple client.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "baXcVq3ii0Cb"
+ },
+ "source": [
+ "# Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "eSHpLxdt1jmE"
+ },
+ "outputs": [],
+ "source": [
+ "import PIL\n",
+ "from flatland.utils.rendertools import RenderTool\n",
+ "import imageio\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>.container { width:95% !important; }</style>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.display import clear_output\n",
+ "from IPython.core import display\n",
+ "import ipywidgets as ipw\n",
+ "display.display(display.HTML(\"<style>.container { width:95% !important; }</style>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "UeX1h4c0i5e6"
+ },
+ "source": [
+ "# Experiments\n",
+ "\n",
+ "This has been mostly changed to load envs using `importlib_resources`. It's getting them from the package \"envdata.tests`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "kAiCO4x75Rbw"
+ },
+ "outputs": [],
+ "source": [
+ "# ENV FILE PATH\n",
+ "#env_file = \"Test_20_Level_0.pkl\"\n",
+ "#env_file = \"../../evaluation_visualization/round2/or-0827/Test_23/Level_1.pkl\"\n",
+ "#env_file = \"../../evaluation_visualization/round2/rl-0827/Test_23/Level_1.pkl\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#if os.path.exists(\"../env_data\"):\n",
+ "# env_file = \"../env_data/tests/Test_2_Level_0.pkl\"\n",
+ "#else:\n",
+ "# env_file = \"./env_data/tests/Test_2_Level_0.pkl\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sPack, sResource = \"env_data.tests\", \"Test_2_Level_0.pkl\"\n",
+ "sPack, sResource = \"env_data.tests\", \"Test_9_Level_1.pkl\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#env_file = \"../../evaluation_visualization/round2/or-0827/Test_23/Level_1.pkl\"\n",
+ "#env_file = \"../../evaluation_visualization/round2/rl-0827/Test_23/Level_1.pkl\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "\n",
+ "from flatland.envs.rail_env import RailEnv\n",
+ "from flatland.envs.rail_generators import sparse_rail_generator\n",
+ "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
+ "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
+ "from flatland.envs.rail_generators import rail_from_file\n",
+ "from flatland.envs.schedule_generators import schedule_from_file\n",
+ "from flatland.core.env_observation_builder import DummyObservationBuilder"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from flatland.envs.persistence import RailEnvPersister\n",
+ "from flatland.evaluators.client import FlatlandRemoteClient\n",
+ "import redis\n",
+ "import subprocess as sp\n",
+ "import shlex\n",
+ "import time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/home/jeremy/projects/aicrowd/rl-trains/flatland5/notebooks\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pwd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Clear any old redis keys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oRedis = redis.Redis()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lKeys = oRedis.keys(\"flatland*\")\n",
+ "lKeys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for sKey in lKeys:\n",
+ " print(sKey)\n",
+ " oRedis.delete(sKey)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Service python command\n",
+ "### Kill any old service process"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ps -ef | grep -i python | grep -i flatland.evaluators.service | awk '{print $2}' | xargs kill"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sCmd = \"python -m flatland.evaluators.service --test_folder ../env_data/tests/service_test --mergeDir ./tmp/merge --actionDir ./tmp/actions --pickle --missingOnly\"\n",
+ "sCmd = \"python -m flatland.evaluators.service --test_folder ../env_data/tests/service_test --pickle --verbose\"\n",
+ "lsCmd = shlex.split(sCmd)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#wOut = ipw.Output()\n",
+ "#wOut"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oPipe = sp.Popen(lsCmd)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "subprocess.Popen"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(oPipe)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oPipe.poll()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Request : {'type': 'FLATLAND_RL.PING', 'payload': {'version': '2.2.1'}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::c26d0b28a6e1a7c59f3e022fa56a8b77', 'timestamp': 1601382710.231949}\n",
+ "Response : b'\\x80\\x03}q\\x00(X\\x04\\x00\\x00\\x00typeq\\x01X\\x10\\x00\\x00\\x00FLATLAND_RL.PONGq\\x02X\\x07\\x00\\x00\\x00payloadq\\x03}q\\x04u.'\n"
+ ]
+ }
+ ],
+ "source": [
+ "oFRC = FlatlandRemoteClient(test_envs_root=\"../env_data/tests/service_test/\", verbose=True, use_pickle=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env, env_dict = RailEnvPersister.load_new(\"../env_data/tests/service_test/Test_0/Level_0.pkl\") # env_file)\n",
+ "ldActions = env_dict[\"actions\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def my_controller(obs, _env):\n",
+ " dAct = ldActions[_env._elapsed_steps]\n",
+ " #_action = {}\n",
+ " #for _idx, _ in enumerate(_env.agents):\n",
+ " # _action[_idx] = np.random.randint(0, 5)\n",
+ " return dAct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oObsB = DummyObservationBuilder()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "oObsB.get()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Request : {'type': 'FLATLAND_RL.ENV_CREATE', 'payload': {}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::c5f9c9736b4a07080d62491b55587291', 'timestamp': 1601382711.3886104}\n",
+ "Response : b'\\x80\\x03}q\\x00(X\\x04\\x00\\x00\\x00typeq\\x01X\\x1f\\x00\\x00\\x00FLATLAND_RL.ENV_CREATE_RESPONSEq\\x02X\\x07\\x00\\x00\\x00payloadq\\x03}q\\x04(X\\x0b\\x00\\x00\\x00observationq\\x05\\x88X\\r\\x00\\x00\\x00env_file_pathq\\x06X\\x12\\x00\\x00\\x00Test_0/Level_0.pklq\\x07X\\x04\\x00\\x00\\x00infoq\\x08}q\\t(X\\x0f\\x00\\x00\\x00action_requiredq\\n}q\\x0b(K\\x00\\x88K\\x01\\x88K\\x02\\x88K\\x03\\x88K\\x04\\x88K\\x05\\x88K\\x06\\x88K\\x07\\x88K\\x08\\x88K\\t\\x88uX\\x0b\\x00\\x00\\x00malfunctionq\\x0c}q\\r(K\\x00K\\x00K\\x01K\\x00K\\x02K\\x00K\\x03K\\x00K\\x04K\\x00K\\x05K\\x00K\\x06K\\x00K\\x07K\\x00K\\x08K\\x00K\\tK\\x00uX\\x05\\x00\\x00\\x00speedq\\x0e}q\\x0f(K\\x00G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x01G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x02G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x03G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x04G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x05G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x06G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x07G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x08G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\tG?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00uX\\x06\\x00\\x00\\x00statusq\\x10}q\\x11(K\\x00cflatland.envs.agent_utils\\nRailAgentStatus\\nq\\x12K\\x00\\x85q\\x13Rq\\x14K\\x01h\\x14K\\x02h\\x14K\\x03h\\x14K\\x04h\\x14K\\x05h\\x14K\\x06h\\x14K\\x07h\\x14K\\x08h\\x14K\\th\\x14uuX\\x0b\\x00\\x00\\x00random_seedq\\x15M\\xe9\\x03uu.'\n",
+ "Received Env : Test_0/Level_0.pkl\n",
+ "Current env path : ../env_data/tests/service_test/Test_0/Level_0.pkl\n",
+ "DEPRECATED - use FileMalfunctionGen instead of malfunction_from_file\n",
+ "DEPRECATED - RailEnv arg: malfunction_and_process_data - use malfunction_generator\n",
+ "Episode : 0\n",
+ "False\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 7.510185241699219e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::fcbc1a592db4b9ab83ee4b82489be98d', 'timestamp': 1601382711.4002197}\n",
+ "Step Time : 0.0022978782653808594\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 7.724761962890625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::01660f051b3f375a8a00683810f55df6', 'timestamp': 1601382711.4025884}\n",
+ "Step Time : 0.003253936767578125\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 1, 3: 2, 8: 4}, 'inference_time': 0.0001881122589111328}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::8f8a0507d4ce4aaff58d3754c2470535', 'timestamp': 1601382711.4060314}\n",
+ "Step Time : 0.0032224655151367188\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 4, 3: 4, 8: 1}, 'inference_time': 8.392333984375e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::dcbe785e0485cdea9eea2635cf50ff2e', 'timestamp': 1601382711.4093235}\n",
+ "Step Time : 0.0022280216217041016\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 3, 3: 1, 8: 3}, 'inference_time': 9.489059448242188e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::84fe3e3f27788eafc3e651a0b2dc6381', 'timestamp': 1601382711.4116464}\n",
+ "Step Time : 0.0026159286499023438\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 3, 8: 4}, 'inference_time': 0.00020122528076171875}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::c6c940aa9ecedb1148ee20bb3fdb4137', 'timestamp': 1601382711.414452}\n",
+ "Step Time : 0.0015065670013427734\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 1}, 'inference_time': 5.6743621826171875e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::b31263b6c34f9fae6a601b49dcb269cf', 'timestamp': 1601382711.4160109}\n",
+ "Step Time : 0.0012655258178710938\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 4, 8: 4}, 'inference_time': 4.029273986816406e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::989cb1bec7ffe8eb46654f97c9c5c3d8', 'timestamp': 1601382711.4173107}\n",
+ "Step Time : 0.0013382434844970703\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 3, 3: 2, 8: 4}, 'inference_time': 0.0005276203155517578}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::5953731bab617e774304306fe227cb76', 'timestamp': 1601382711.41918}\n",
+ "Step Time : 0.003258943557739258\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 3}, 'inference_time': 0.0005054473876953125}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::7a5baaa32fae1f442e5d5097e8e7bd61', 'timestamp': 1601382711.422997}\n",
+ "Step Time : 0.002592802047729492\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 3, 8: 4}, 'inference_time': 0.00022459030151367188}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::9233d209716f4ae78a5dbe124de67e27', 'timestamp': 1601382711.4257793}\n",
+ "Step Time : 0.0035974979400634766\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 3}, 'inference_time': 8.940696716308594e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::86a6797d4a4dc741a0d88243b124638c', 'timestamp': 1601382711.4294176}\n",
+ "Step Time : 0.0015513896942138672\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {3: 2, 1: 2, 8: 4}, 'inference_time': 5.1021575927734375e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::5d70186b357a8e3b6e47f4b7d75e8c25', 'timestamp': 1601382711.431004}\n",
+ "Step Time : 0.0012707710266113281\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {3: 2, 1: 4, 8: 4}, 'inference_time': 3.838539123535156e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::2872c8d42231e56eba7350e8ee58fb0f', 'timestamp': 1601382711.4323094}\n",
+ "Step Time : 0.0012013912200927734\n",
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+ "Step Time : 0.0010089874267578125\n",
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+ "Step Time : 0.00109100341796875\n",
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+ "Step Time : 0.0007436275482177734\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 2, 9: 2}, 'inference_time': 3.6716461181640625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::42c1899fc8f66af0a518899e5f4b8095', 'timestamp': 1601382711.7646654}\n",
+ "Step Time : 0.0007314682006835938\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 2}, 'inference_time': 3.910064697265625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::d4ffadcd395448a903402e38bc0a84e7', 'timestamp': 1601382711.765435}\n",
+ "Step Time : 0.0006229877471923828\n",
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+ "Step Time : 0.0006310939788818359\n",
+ "Current Episode : 1\n",
+ "Episode Done\n",
+ "Reward : 10.0\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_CREATE', 'payload': {}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::978da518f555337ce6430a1a07b61df8', 'timestamp': 1601382711.7667947}\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Response : b'\\x80\\x03}q\\x00(X\\x04\\x00\\x00\\x00typeq\\x01X\\x1f\\x00\\x00\\x00FLATLAND_RL.ENV_CREATE_RESPONSEq\\x02X\\x07\\x00\\x00\\x00payloadq\\x03}q\\x04(X\\x0b\\x00\\x00\\x00observationq\\x05\\x88X\\r\\x00\\x00\\x00env_file_pathq\\x06X\\x12\\x00\\x00\\x00Test_0/Level_1.pklq\\x07X\\x04\\x00\\x00\\x00infoq\\x08}q\\t(X\\x0f\\x00\\x00\\x00action_requiredq\\n}q\\x0b(K\\x00\\x88K\\x01\\x88K\\x02\\x88K\\x03\\x88K\\x04\\x88K\\x05\\x88K\\x06\\x88K\\x07\\x88K\\x08\\x88K\\t\\x88uX\\x0b\\x00\\x00\\x00malfunctionq\\x0c}q\\r(K\\x00K\\x00K\\x01K\\x00K\\x02K\\x00K\\x03K\\x00K\\x04K\\x00K\\x05K\\x00K\\x06K\\x00K\\x07K\\x00K\\x08K\\x00K\\tK\\x00uX\\x05\\x00\\x00\\x00speedq\\x0e}q\\x0f(K\\x00G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x01G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x02G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x03G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x04G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x05G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x06G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x07G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x08G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\tG?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00uX\\x06\\x00\\x00\\x00statusq\\x10}q\\x11(K\\x00cflatland.envs.agent_utils\\nRailAgentStatus\\nq\\x12K\\x00\\x85q\\x13Rq\\x14K\\x01h\\x14K\\x02h\\x14K\\x03h\\x14K\\x04h\\x14K\\x05h\\x14K\\x06h\\x14K\\x07h\\x14K\\x08h\\x14K\\th\\x14uuX\\x0b\\x00\\x00\\x00random_seedq\\x15M\\xe9\\x03uu.'\n",
+ "Received Env : Test_0/Level_1.pkl\n",
+ "Current env path : ../env_data/tests/service_test/Test_0/Level_1.pkl\n",
+ "DEPRECATED - use FileMalfunctionGen instead of malfunction_from_file\n",
+ "DEPRECATED - RailEnv arg: malfunction_and_process_data - use malfunction_generator\n",
+ "Episode : 1\n",
+ "False\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 7.557868957519531e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::fcbc1a592db4b9ab83ee4b82489be98d', 'timestamp': 1601382711.8173141}\n",
+ "Step Time : 0.002300262451171875\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 9.107589721679688e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::01660f051b3f375a8a00683810f55df6', 'timestamp': 1601382711.8197002}\n",
+ "Step Time : 0.0027971267700195312\n",
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+ "Step Time : 0.003201723098754883\n",
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+ "Step Time : 0.00098419189453125\n",
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+ "Step Time : 0.0015869140625\n",
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+ "Step Time : 0.0019025802612304688\n",
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+ "Step Time : 0.00093841552734375\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 2}, 'inference_time': 4.029273986816406e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::d4ffadcd395448a903402e38bc0a84e7', 'timestamp': 1601382712.1766286}\n",
+ "Step Time : 0.0007486343383789062\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 2}, 'inference_time': 3.695487976074219e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::ea261726296cb7909bdebb41e425c339', 'timestamp': 1601382712.1774101}\n",
+ "Step Time : 0.0007071495056152344\n",
+ "Current Episode : 2\n",
+ "Episode Done\n",
+ "Reward : 10.0\n",
+ "Request : {'type': 'FLATLAND_RL.ENV_CREATE', 'payload': {}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::978da518f555337ce6430a1a07b61df8', 'timestamp': 1601382712.1781914}\n",
+ "Response : b'\\x82\\xa4type\\xb1FLATLAND_RL.ERROR\\xa7payload\\x82\\xa4type\\xb1FLATLAND_RL.ERROR\\xa7payload\\xb7list index out of range'\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "unregistered extension code 164",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-26-c2b4e928c1b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m obs, info = oFRC.env_create(\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mobs_builder_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moObsB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m )\n\u001b[1;32m 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home3/jeremy/projects/aicrowd/rl-trains/flatland5/flatland/evaluators/client.py\u001b[0m in \u001b[0;36menv_create\u001b[0;34m(self, obs_builder_object)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0m_request\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmessages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFLATLAND_RL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENV_CREATE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0m_request\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'payload'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m \u001b[0m_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remote_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 237\u001b[0m \u001b[0mobservation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_response\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'payload'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'observation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_response\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'payload'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'info'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home3/jeremy/projects/aicrowd/rl-trains/flatland5/flatland/evaluators/client.py\u001b[0m in \u001b[0;36m_remote_request\u001b[0;34m(self, _request, blocking)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Response : \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_pickle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m \u001b[0m_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 193\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m _response = msgpack.unpackb(\n",
+ "\u001b[0;31mValueError\u001b[0m: unregistered extension code 164"
+ ]
+ }
+ ],
+ "source": [
+ "if True:\n",
+ " episode = 0\n",
+ " obs = True\n",
+ " while obs:\n",
+ " obs, info = oFRC.env_create(\n",
+ " obs_builder_object=oObsB\n",
+ " )\n",
+ " if not obs:\n",
+ " print(\"null observation!\")\n",
+ " \"\"\"\n",
+ " The remote env returns False as the first obs\n",
+ " when it is done evaluating all the individual episodes\n",
+ " \"\"\"\n",
+ " break\n",
+ " print(\"Episode : {}\".format(episode))\n",
+ " episode += 1\n",
+ "\n",
+ " print(oFRC.env.dones['__all__'])\n",
+ "\n",
+ " while True:\n",
+ " action = my_controller(obs, oFRC.env)\n",
+ " time_start = time.time()\n",
+ "\n",
+ " try:\n",
+ " observation, all_rewards, done, info = oFRC.env_step(action)\n",
+ " time_diff = time.time() - time_start\n",
+ " print(\"Step Time : \", time_diff)\n",
+ " if done['__all__']:\n",
+ " print(\"Current Episode : \", episode)\n",
+ " print(\"Episode Done\")\n",
+ " print(\"Reward : \", sum(list(all_rewards.values())))\n",
+ " break\n",
+ " except TimeoutException as err:\n",
+ " print(\"Timeout: \", err)\n",
+ " break\n",
+ "\n",
+ " print(\"Evaluation Complete...\")\n",
+ " print(oFRC.submit())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oFRC.env"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Render episode stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env, env_dict = RailEnvPersister.load_new(sResource, load_from_package=sPack) # env_file)\n",
+ "\n",
+ "# the seed has to match that used to record the episode, in order for the malfunctions to match.\n",
+ "env.reset(random_seed=1001)\n",
+ "oRT = RenderTool(env, show_debug=True)\n",
+ "aImg = oRT.render_env(show_rowcols=True, return_image=True, show_inactive_agents=True)\n",
+ "print(env._max_episode_steps)\n",
+ "PIL.Image.fromarray(aImg)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "loAgs = env_dict[\"agents\"]\n",
+ "lCols = \"initial_direction,direction,initial_position,position\".split(\",\")\n",
+ "pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols] \n",
+ " for oAg in loAgs], columns=lCols)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols] \n",
+ " for oAg in env.agents], columns=lCols)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.DataFrame([ vars(oAg) for oAg in env.agents])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "kkejL06T8xyU"
+ },
+ "outputs": [],
+ "source": [
+ "# from persistence.py\n",
+ "def get_agent_state(env):\n",
+ " list_agents_state = []\n",
+ " for iAg, oAg in enumerate(env.agents):\n",
+ " # the int cast is to avoid numpy types which may cause problems with msgpack\n",
+ " # in env v2, agents may have position None, before starting\n",
+ " if oAg.position is None:\n",
+ " pos = (0, 0)\n",
+ " else:\n",
+ " pos = (int(oAg.position[0]), int(oAg.position[1]))\n",
+ " # print(\"pos:\", pos, type(pos[0]))\n",
+ " list_agents_state.append(\n",
+ " [*pos, int(oAg.direction), oAg.malfunction_data[\"malfunction\"]])\n",
+ " \n",
+ " return list_agents_state"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "uNmOgQjO-Rw_"
+ },
+ "outputs": [],
+ "source": [
+ "expert_actions = env_dict['actions']\n",
+ "episode_states = env_dict['episode']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.DataFrame([ vars(oAg) for oAg in env.agents])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "colab_type": "code",
+ "id": "IXQxfUXF2U33",
+ "outputId": "2a94ffec-c6b7-4cc3-d779-8c430d66918d",
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "env_renderer = RenderTool(env, gl=\"PGL\", show_debug=True)\n",
+ "\n",
+ "n_agents = env.get_num_agents()\n",
+ "x_dim, y_dim = env.width, env.height\n",
+ "max_steps = env_dict['max_episode_steps']\n",
+ "\n",
+ "action_dict = {}\n",
+ "frames = []\n",
+ "\n",
+ "# log everything in original state\n",
+ "statuses = []\n",
+ "for a in range(n_agents):\n",
+ " statuses.append(env.agents[a].status)\n",
+ "pilImg = render_env(env_renderer)\n",
+ "frames.append({\n",
+ " 'image': pilImg,\n",
+ " 'statuses': statuses\n",
+ " })\n",
+ "\n",
+ "step = 0\n",
+ "all_done = False\n",
+ "failed_action_check = False\n",
+ "print(\"Processing episode steps:\")\n",
+ "while not all_done and step < len(expert_actions):\n",
+ " print(step, end=\", \")\n",
+ " \"\"\"\n",
+ " for a in range(n_agents):\n",
+ " if info['action_required'][a]:\n",
+ " if step < len(expert_actions):\n",
+ " if a in expert_actions[step]:\n",
+ " action = expert_actions[step][a]\n",
+ " else:\n",
+ " print(\"Step {}: agent {} needs action but not provided! only got {}\".format(step, a, expert_actions[step]))\n",
+ " else:\n",
+ " action = 0\n",
+ "\n",
+ " action_dict.update({a: action})\n",
+ " \"\"\"\n",
+ " \n",
+ " if step < len(expert_actions):\n",
+ " dAct = expert_actions[step]\n",
+ " else:\n",
+ " dAct = {}\n",
+ " \n",
+ " next_obs, all_rewards, done, info = env.step(dAct)\n",
+ " \n",
+ " if True:\n",
+ " # Check that agent states match recorded states\n",
+ " if get_agent_state(env) == episode_states[step]:\n",
+ " pass\n",
+ " else:\n",
+ " print(\"MISMATCH\")\n",
+ " failed_action_check = True\n",
+ " #print(\"env:\", get_agent_state(env))\n",
+ " #print(\"epi:\", episode_states[step])\n",
+ " llAgSt = get_agent_state(env)\n",
+ " llEpSt = episode_states[step]\n",
+ " for iAg, (lAgSt, lEpSt) in enumerate(zip(llAgSt, llEpSt)):\n",
+ " if lAgSt != lEpSt:\n",
+ " print(\"Ag:\", iAg, \"Env: \", lAgSt, \"Epi:\", lEpSt, end = \"; \")\n",
+ " print(\"------\")\n",
+ "\n",
+ " \n",
+ " # Force agent states from the recorded states\n",
+ " if False:\n",
+ " for idx, agent in enumerate(env.agents):\n",
+ " #print(episode_states[step][idx])\n",
+ " rcPos = episode_states[step][idx][0:2]\n",
+ " #print(idx, rcPos)\n",
+ " if rcPos == [0,0]:\n",
+ " agent.position = None\n",
+ " else:\n",
+ " agent.position = (*rcPos,) # episode_states[step][idx][0], episode_states[step][idx][1]#, episode_states[step][idx][2]\n",
+ "\n",
+ " agent.malfunction_data[\"malfunction\"] = episode_states[step][idx][3]\n",
+ " agent.direction = int(episode_states[step][idx][2])\n",
+ "\n",
+ " agent.old_direction = int(episode_states[step-1][idx][2])\n",
+ " agent.old_position = episode_states[step-1][idx][:2]\n",
+ "\n",
+ " statuses = []\n",
+ " for a in range(n_agents):\n",
+ " statuses.append(env.agents[a].status)\n",
+ "\n",
+ " #clear_output(wait=True)\n",
+ " pilImg = render_env(env_renderer)\n",
+ " frames.append({\n",
+ " 'image': pilImg,\n",
+ " 'statuses': statuses\n",
+ " })\n",
+ " #print(\"Replaying {}/{}\".format(step, max_steps))\n",
+ "\n",
+ " if done['__all__']:\n",
+ " all_done = True\n",
+ " max_steps = step + 1\n",
+ " print(\"done\")\n",
+ "\n",
+ " step += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert failed_action_check == False, \"Realised states did not match stored states.\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env.agents[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "[ oAg.malfunction_data for oAg in env_dict[\"agents\"] ][:3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env_dict[\"malfunction\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env._max_episode_steps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sfImg = sResource.replace(\"pkl\", \"gif\")\n",
+ "imageio.mimsave(sfImg, [d[\"image\"] for d in frames], subrectangles=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sfImg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display.Image(sfImg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 577,
+ "referenced_widgets": [
+ "11ecd9887b824225b037d043745c7f19",
+ "d46bfa4e89d44814acb57a247aa60fec",
+ "711e232d2cb54266a52552d90ce3f05e",
+ "176dbef2d38244bcb256258a692d53a7",
+ "81330f0e89e149e697e26eb0ed80d023",
+ "b60ecb9134eb449088fd35d3ee9dd20e",
+ "0a2d6b655cc6486bba4cb2ed086b2a19",
+ "6870e3259fa24df5b6117eadcba5a84f",
+ "3a991f2384ec4a0984a850c380fdf8c5",
+ "707d4424f540458dbb43cd52177eddf3",
+ "c17016ecd7e24c20a2240f5cb8e1bca1",
+ "c7d9b8f474144b8b8311ab87338efc64"
+ ]
+ },
+ "colab_type": "code",
+ "id": "uFzeelj2FW9n",
+ "outputId": "3b65f192-301d-4c98-cf0c-c987433003ef"
+ },
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact, interactive, fixed, interact_manual, Play\n",
+ "import ipywidgets as widgets\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import numpy as np\n",
+ "from IPython.display import HTML\n",
+ "\n",
+ "display.display(HTML('<link rel=\"stylesheet\" href=\"//stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css\"/>'))\n",
+ "\n",
+ "def plot_func(frame_idx):\n",
+ " frame = frames[int(frame_idx)]\n",
+ " display.display(frame['image'])\n",
+ " #print(frame['statuses'])\n",
+ "\n",
+ "if True:\n",
+ " slider = widgets.FloatSlider(value=0, min=0, max=max_steps, step=1)\n",
+ " interact(plot_func, frame_idx = slider)\n",
+ "\n",
+ " play = Play(\n",
+ " max=max_steps,\n",
+ " value=0,\n",
+ " step=1,\n",
+ " interval=250\n",
+ " )\n",
+ "\n",
+ " widgets.link((play, 'value'), (slider, 'value'))\n",
+ " widgets.VBox([play])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "j9_J2f6K64Jb"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g3Ep = np.array(episode_states)\n",
+ "np.sum(g3Ep[:,:,3] > 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(np.sum(g3Ep[:,:,3]>0, axis=1))\n",
+ "plt.title(sResource + \"\\nmalfunctioning agents by time step\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g3Ep.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g3Ep2 = np.array(env.cur_episode)\n",
+ "g3Ep2.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.step(range(len(g3Ep2)), np.sum(g3Ep2[:,:,4]==1, axis=1), label=\"Active\")\n",
+ "plt.step(range(len(g3Ep2)), np.sum(g3Ep2[:,:,4]==0, axis=1), label=\"Ready to depart\")\n",
+ "plt.title(\"env: \"+ sResource +\"\\nActive Agents by timestep\")\n",
+ "plt.legend()"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Flatland Round 2 Replays",
+ "provenance": []
+ },
+ "hide_input": false,
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.7"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
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