diff --git a/RLLib_training/RailEnvRLLibWrapper.py b/RLLib_training/RailEnvRLLibWrapper.py
index 6d34e95e6b1e0fdada612fffcbd00c6e18a5ba8e..0fd0f59727bb384a4545b8189e7844d8540367b1 100644
--- a/RLLib_training/RailEnvRLLibWrapper.py
+++ b/RLLib_training/RailEnvRLLibWrapper.py
@@ -5,6 +5,8 @@ from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils.seed import seed as set_seed
+
+
class RailEnvRLLibWrapper(MultiAgentEnv):
def __init__(self, config):
@@ -18,13 +20,15 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
self.predefined_env = False
if config['rail_generator'] == "complex_rail_generator":
- self.rail_generator = complex_rail_generator(nr_start_goal=config['number_of_agents'], min_dist=5,
+ self.rail_generator = complex_rail_generator(nr_start_goal=config['number_of_agents'],
+ min_dist=config['min_dist'],
nr_extra=config['nr_extra'],
seed=config['seed'] * (1 + vector_index))
elif config['rail_generator'] == "random_rail_generator":
self.rail_generator = random_rail_generator()
elif config['rail_generator'] == "load_env":
self.predefined_env = True
+ self.rail_generator = random_rail_generator()
else:
raise (ValueError, f'Unknown rail generator: {config["rail_generator"]}')
@@ -32,12 +36,11 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
set_seed(config['seed'] * (1 + vector_index))
self.env = RailEnv(width=config["width"], height=config["height"],
number_of_agents=config["number_of_agents"],
- obs_builder_object=config['obs_builder'], rail_generator=self.rail_generator,
- prediction_builder_object=config['predictor'])
+ obs_builder_object=config['obs_builder'], rail_generator=self.rail_generator)
if self.predefined_env:
- self.env.load(config['load_env_path'])
- self.env.load_resource('torch_training.railway', config['load_env_path'])
+ # self.env.load(config['load_env_path'])
+ self.env.load_resource('torch_training.railway', 'complex_scene.pkl')
self.width = self.env.width
self.height = self.env.height
@@ -50,25 +53,28 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
else:
obs = self.env.reset()
- predictions = self.env.predict()
- if predictions != {}:
- # pred_pos is a 3 dimensions array (N_Agents, T_pred, 2) containing x and y coordinates of
- # agents at each time step
- pred_pos = np.concatenate([[x[:, 1:3]] for x in list(predictions.values())], axis=0)
- pred_dir = [x[:, 2] for x in list(predictions.values())]
+
+ # predictions = self.env.predict()
+ # if predictions != {}:
+ # # pred_pos is a 3 dimensions array (N_Agents, T_pred, 2) containing x and y coordinates of
+ # # agents at each time step
+ # pred_pos = np.concatenate([[x[:, 1:3]] for x in list(predictions.values())], axis=0)
+ # pred_dir = [x[:, 2] for x in list(predictions.values())]
o = dict()
for i_agent in range(len(self.env.agents)):
-
- if predictions != {}:
- pred_obs = self.get_prediction_as_observation(pred_pos, pred_dir, i_agent)
-
- agent_id_one_hot = np.zeros(len(self.env.agents))
- agent_id_one_hot[i_agent] = 1
- o[i_agent] = [obs[i_agent], agent_id_one_hot, pred_obs]
- else:
- o[i_agent] = obs[i_agent]
+ data, distance, agent_data = self.env.obs_builder.split_tree(tree=np.array(obs[i_agent]),
+ num_features_per_node=8, current_depth=0)
+ # if predictions != {}:
+ # pred_obs = self.get_prediction_as_observation(pred_pos, pred_dir, i_agent)
+ #
+ # agent_id_one_hot = np.zeros(len(self.env.agents))
+ # agent_id_one_hot[i_agent] = 1
+ # o[i_agent] = [obs[i_agent], agent_id_one_hot, pred_obs]
+ # else:
+
+ o[i_agent] = [data, distance, agent_data]
# needed for the renderer
self.rail = self.env.rail
@@ -93,23 +99,25 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
r = dict()
o = dict()
- predictions = self.env.predict()
- if predictions != {}:
- # pred_pos is a 3 dimensions array (N_Agents, T_pred, 2) containing x and y coordinates of
- # agents at each time step
- pred_pos = np.concatenate([[x[:, 1:3]] for x in list(predictions.values())], axis=0)
- pred_dir = [x[:, 2] for x in list(predictions.values())]
+ # predictions = self.env.predict()
+ # if predictions != {}:
+ # # pred_pos is a 3 dimensions array (N_Agents, T_pred, 2) containing x and y coordinates of
+ # # agents at each time step
+ # pred_pos = np.concatenate([[x[:, 1:3]] for x in list(predictions.values())], axis=0)
+ # pred_dir = [x[:, 2] for x in list(predictions.values())]
for i_agent in range(len(self.env.agents)):
if i_agent not in self.agents_done:
-
- if predictions != {}:
- pred_obs = self.get_prediction_as_observation(pred_pos, pred_dir, i_agent)
- agent_id_one_hot = np.zeros(len(self.env.agents))
- agent_id_one_hot[i_agent] = 1
- o[i_agent] = [obs[i_agent], agent_id_one_hot, pred_obs]
- else:
- o[i_agent] = obs[i_agent]
+ data, distance, agent_data = self.env.obs_builder.split_tree(tree=np.array(obs[i_agent]),
+ num_features_per_node=8, current_depth=0)
+
+ # if predictions != {}:
+ # pred_obs = self.get_prediction_as_observation(pred_pos, pred_dir, i_agent)
+ # agent_id_one_hot = np.zeros(len(self.env.agents))
+ # agent_id_one_hot[i_agent] = 1
+ # o[i_agent] = [obs[i_agent], agent_id_one_hot, pred_obs]
+ # else:
+ o[i_agent] = [data, distance, agent_data]
r[i_agent] = rewards[i_agent]
d[i_agent] = dones[i_agent]
diff --git a/RLLib_training/custom_preprocessors.py b/RLLib_training/custom_preprocessors.py
index bd45dfd2bd8620c2866ac3d079a9e82dd4c20c7e..41e895bee95cde7bc59220ce2eee4eaabd458651 100644
--- a/RLLib_training/custom_preprocessors.py
+++ b/RLLib_training/custom_preprocessors.py
@@ -23,7 +23,7 @@ def min_lt(seq, val):
min = np.inf
idx = len(seq) - 1
while idx >= 0:
- if seq[idx] > val and seq[idx] < min:
+ if seq[idx] >= val and seq[idx] < min:
min = seq[idx]
idx -= 1
return min
@@ -38,7 +38,8 @@ def norm_obs_clip(obs, clip_min=-1, clip_max=1):
:return: returnes normalized and clipped observatoin
"""
max_obs = max(1, max_lt(obs, 1000))
- min_obs = max(0, min_lt(obs, 0))
+ min_obs = min(max_obs, min_lt(obs, 0))
+
if max_obs == min_obs:
return np.clip(np.array(obs) / max_obs, clip_min, clip_max)
norm = np.abs(max_obs - min_obs)
@@ -49,12 +50,22 @@ def norm_obs_clip(obs, clip_min=-1, clip_max=1):
class CustomPreprocessor(Preprocessor):
def _init_shape(self, obs_space, options):
+ return sum([space.shape[0] for space in obs_space]),
# return (sum([space.shape[0] for space in obs_space]), )
- return ((sum([space.shape[0] for space in obs_space[:2]]) + obs_space[2].shape[0] * obs_space[2].shape[1]),)
+ # return ((sum([space.shape[0] for space in obs_space[:2]]) + obs_space[2].shape[0] * obs_space[2].shape[1]),)
def transform(self, observation):
+ print('OBSSSSSSSSSSSSSSSSSs', observation, observation.shape)
+ data = norm_obs_clip(observation[0])
+ distance = norm_obs_clip(observation[1])
+ agent_data = np.clip(observation[2], -1, 1)
+
+ return np.concatenate((np.concatenate((data, distance)), agent_data))
+ return norm_obs_clip(observation)
+ return np.concatenate([norm_obs_clip(observation[0]), norm_obs_clip(observation[1])])
# if len(observation) == 111:
# return np.concatenate([norm_obs_clip(obs) for obs in observation])
+ # print('OBSERVATION:', observation, len(observation[0]))
return np.concatenate([norm_obs_clip(observation[0]), observation[1], observation[
2].flatten()]) #, norm_obs_clip(observation[1]), observation[2], observation[3].flatten()])
#one_hot = observation[-3:]
diff --git a/RLLib_training/experiment_configs/predictions_test/config.gin b/RLLib_training/experiment_configs/predictions_test/config.gin
new file mode 100644
index 0000000000000000000000000000000000000000..b5923dfa1de6aacff716dc3054e2b81e4f66a6ab
--- /dev/null
+++ b/RLLib_training/experiment_configs/predictions_test/config.gin
@@ -0,0 +1,28 @@
+run_experiment.name = "memory_experiment_results"
+run_experiment.num_iterations = 2002
+run_experiment.save_every = 50
+run_experiment.hidden_sizes = [32, 32]
+
+run_experiment.map_width = {"grid_search": [8, 10, 12, 14]}
+run_experiment.map_height = 8
+run_experiment.n_agents = 3
+run_experiment.rail_generator = "complex_rail_generator"
+run_experiment.nr_extra = 1
+run_experiment.policy_folder_name = "ppo_policy_with_pred_map_size_{config[map_width]}"
+
+run_experiment.horizon = 50
+run_experiment.seed = 123
+
+#run_experiment.conv_model = {"grid_search": [True, False]}
+run_experiment.conv_model = False
+
+run_experiment.obs_builder = @TreeObsForRailEnv()
+TreeObsForRailEnv.max_depth = 2
+TreeObsForRailEnv.predictor = @DummyPredictorForRailEnv()
+LocalObsForRailEnv.view_radius = 5
+
+run_experiment.entropy_coeff = 1e-3
+run_experiment.kl_coeff = 0.2
+run_experiment.lambda_gae = 0.9
+#run_experiment.predictor = "dummy_predictor"
+run_experiment.step_memory = 1
diff --git a/RLLib_training/render_training_result.py b/RLLib_training/render_training_result.py
index 5f0159fa70e975b80a17a296a82139470a645f48..1719f4403316355ebfb063fc1b16baea66d476c4 100644
--- a/RLLib_training/render_training_result.py
+++ b/RLLib_training/render_training_result.py
@@ -50,11 +50,11 @@ ModelCatalog.register_custom_model("conv_model", ConvModelGlobalObs)
ray.init()#object_store_memory=150000000000, redis_max_memory=30000000000)
-CHECKPOINT_PATH = '/home/guillaume/Desktop/distMAgent/env_complexity_benchmark/' \
- 'ppo_policy_nr_extra_10_0qxx0qy_/checkpoint_1001/checkpoint-1001'
+CHECKPOINT_PATH = '/home/guillaume/Desktop/distMAgent/experiment_agent_memory/' \
+ 'ppo_policy_hidden_size_32_entropy_coeff_0.0001_mu413rlu/checkpoint_201/checkpoint-201'
N_EPISODES = 10
-N_STEPS_PER_EPISODE = 80
+N_STEPS_PER_EPISODE = 50
def render_training_result(config):
@@ -65,16 +65,24 @@ def render_training_result(config):
# Example configuration to generate a random rail
env_config = {"width": config['map_width'],
"height": config['map_height'],
- "rail_generator": config["rail_generator"],
+ "rail_generator": "load_env",#config["rail_generator"],
"nr_extra": config["nr_extra"],
"number_of_agents": config['n_agents'],
"seed": config['seed'],
- "obs_builder": config['obs_builder']}
-
+ "obs_builder": config['obs_builder'],
+ "predictor": config["predictor"],
+ "step_memory": config["step_memory"]}
# Observation space and action space definitions
if isinstance(config["obs_builder"], TreeObsForRailEnv):
- obs_space = gym.spaces.Box(low=-1, high=1, shape=(147,))
+ if config['predictor'] is None:
+ obs_space = gym.spaces.Tuple(
+ (gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(147,)),) * config['step_memory'])
+ else:
+ obs_space = gym.spaces.Tuple((gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(147,)),
+ gym.spaces.Box(low=0, high=1, shape=(config['n_agents'],)),
+ gym.spaces.Box(low=0, high=1, shape=(20, config['n_agents'])),) * config[
+ 'step_memory'])
preprocessor = "tree_obs_prep"
elif isinstance(config["obs_builder"], GlobalObsForRailEnv):
@@ -109,7 +117,7 @@ def render_training_result(config):
else:
raise ValueError("Undefined observation space")
- act_space = gym.spaces.Discrete(4)
+ act_space = gym.spaces.Discrete(5)
# Dict with the different policies to train
policy_graphs = {
@@ -131,10 +139,11 @@ def render_training_result(config):
"policies_to_train": list(policy_graphs.keys())}
trainer_config["horizon"] = config['horizon']
+
trainer_config["num_workers"] = 0
- trainer_config["num_cpus_per_worker"] = 3
- trainer_config["num_gpus"] = 0
- trainer_config["num_gpus_per_worker"] = 0
+ trainer_config["num_cpus_per_worker"] = 4
+ trainer_config["num_gpus"] = 0.2
+ trainer_config["num_gpus_per_worker"] = 0.2
trainer_config["num_cpus_for_driver"] = 1
trainer_config["num_envs_per_worker"] = 1
trainer_config['entropy_coeff'] = config['entropy_coeff']
@@ -145,17 +154,20 @@ def render_training_result(config):
trainer_config['log_level'] = 'WARN'
trainer_config['num_sgd_iter'] = 10
trainer_config['clip_param'] = 0.2
+ trainer_config['kl_coeff'] = config['kl_coeff']
+ trainer_config['lambda'] = config['lambda_gae']
env = RailEnvRLLibWrapper(env_config)
trainer = Trainer(env=RailEnvRLLibWrapper, config=trainer_config)
+ print('hidden sizes:', config['hidden_sizes'])
trainer.restore(CHECKPOINT_PATH)
policy = trainer.get_policy(config['policy_folder_name'].format(**locals()))
- preprocessor = CustomPreprocessor(gym.spaces.Box(low=-1, high=1, shape=(147,)))
+ preprocessor = CustomPreprocessor(obs_space)
env_renderer = RenderTool(env, gl="PIL")
for episode in range(N_EPISODES):
observation = env.reset()
@@ -184,7 +196,8 @@ def render_training_result(config):
@gin.configurable
def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
map_width, map_height, horizon, policy_folder_name, local_dir, obs_builder,
- entropy_coeff, seed, conv_model, rail_generator, nr_extra):
+ entropy_coeff, seed, conv_model, rail_generator, nr_extra, kl_coeff,
+ lambda_gae, predictor, step_memory):
render_training_result(
config={"n_agents": n_agents,
@@ -200,13 +213,17 @@ def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
"seed": seed,
"conv_model": conv_model,
"rail_generator": rail_generator,
- "nr_extra": 10# nr_extra
+ "nr_extra": nr_extra,
+ "kl_coeff": kl_coeff,
+ "lambda_gae": lambda_gae,
+ "predictor": predictor,
+ "step_memory": step_memory
}
)
if __name__ == '__main__':
gin.external_configurable(tune.grid_search)
- dir = '/home/guillaume/EPFL/Master_Thesis/flatland/baselines/RLLib_training/experiment_configs/env_complexity_benchmark' # To Modify
+ dir = '/home/guillaume/EPFL/Master_Thesis/flatland/baselines/RLLib_training/experiment_configs/experiment_agent_memory' # To Modify
gin.parse_config_file(dir + '/config.gin')
run_experiment(local_dir=dir)
diff --git a/RLLib_training/train_experiment.py b/RLLib_training/train_experiment.py
index 006bab121a3e9aab7d9f014a466ad77c1aced82f..cc8debe1b89a315624c99560b63858e66f2dea1e 100644
--- a/RLLib_training/train_experiment.py
+++ b/RLLib_training/train_experiment.py
@@ -34,6 +34,7 @@ gin.external_configurable(LocalObsForRailEnv)
gin.external_configurable(GlobalObsForRailEnvDirectionDependent)
from ray.rllib.models.preprocessors import TupleFlatteningPreprocessor
+import numpy as np
ModelCatalog.register_custom_preprocessor("tree_obs_prep", CustomPreprocessor)
ModelCatalog.register_custom_preprocessor("global_obs_prep", TupleFlatteningPreprocessor)
@@ -44,10 +45,32 @@ ray.init() # object_store_memory=150000000000, redis_max_memory=30000000000)
__file_dirname__ = os.path.dirname(os.path.realpath(__file__))
+def on_episode_start(info):
+ episode = info['episode']
+ map_width = info['env'].envs[0].width
+ map_height = info['env'].envs[0].height
+ episode.horizon = map_width + map_height
+
+
+# def on_episode_step(info):
+# episode = info['episode']
+# print('#########################', episode._agent_reward_history)
+# # print(ds)
+
+
+def on_episode_end(info):
+ episode = info['episode']
+ score = 0
+ for k, v in episode._agent_reward_history.items():
+ score += np.sum(v)
+ score /= (len(episode._agent_reward_history) * 3 * episode.horizon)
+ episode.custom_metrics["score"] = score
+
def train(config, reporter):
print('Init Env')
set_seed(config['seed'], config['seed'], config['seed'])
+ config['map_height'] = config['map_width']
# Example configuration to generate a random rail
env_config = {"width": config['map_width'],
@@ -57,19 +80,24 @@ def train(config, reporter):
"number_of_agents": config['n_agents'],
"seed": config['seed'],
"obs_builder": config['obs_builder'],
- "predictor": config["predictor"],
+ "min_dist": config['min_dist'],
+ # "predictor": config["predictor"],
"step_memory": config["step_memory"]}
# Observation space and action space definitions
if isinstance(config["obs_builder"], TreeObsForRailEnv):
- if config['predictor'] is None:
- obs_space = gym.spaces.Tuple(
- (gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(147,)),) * config['step_memory'])
- else:
- obs_space = gym.spaces.Tuple((gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(147,)),
- gym.spaces.Box(low=0, high=1, shape=(config['n_agents'],)),
- gym.spaces.Box(low=0, high=1, shape=(20, config['n_agents'])),) * config[
- 'step_memory'])
+ obs_space = gym.spaces.Tuple((gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(168,)), ))
+ # gym.spaces.Box(low=0, high=1, shape=(config['n_agents'],)),
+ # gym.spaces.Box(low=0, high=1, shape=(20, config['n_agents'])),) * config[
+ # 'step_memory'])
+ # if config['predictor'] is None:
+ # obs_space = gym.spaces.Tuple(
+ # (gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(147,)),) * config['step_memory'])
+ # else:
+ # obs_space = gym.spaces.Tuple((gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(147,)),
+ # gym.spaces.Box(low=0, high=1, shape=(config['n_agents'],)),
+ # gym.spaces.Box(low=0, high=1, shape=(20, config['n_agents'])),) * config[
+ # 'step_memory'])
preprocessor = "tree_obs_prep"
elif isinstance(config["obs_builder"], GlobalObsForRailEnv):
@@ -124,12 +152,12 @@ def train(config, reporter):
trainer_config['multiagent'] = {"policy_graphs": policy_graphs,
"policy_mapping_fn": policy_mapping_fn,
"policies_to_train": list(policy_graphs.keys())}
- trainer_config["horizon"] = config['horizon']
+ trainer_config["horizon"] = 1.5 * (config['map_width'] + config['map_height'])#config['horizon']
trainer_config["num_workers"] = 0
- trainer_config["num_cpus_per_worker"] = 4
- trainer_config["num_gpus"] = 0.2
- trainer_config["num_gpus_per_worker"] = 0.2
+ trainer_config["num_cpus_per_worker"] = 7
+ trainer_config["num_gpus"] = 0.0
+ trainer_config["num_gpus_per_worker"] = 0.0
trainer_config["num_cpus_for_driver"] = 1
trainer_config["num_envs_per_worker"] = 1
trainer_config['entropy_coeff'] = config['entropy_coeff']
@@ -142,6 +170,10 @@ def train(config, reporter):
trainer_config['clip_param'] = 0.2
trainer_config['kl_coeff'] = config['kl_coeff']
trainer_config['lambda'] = config['lambda_gae']
+ trainer_config['callbacks'] = {
+ "on_episode_start": tune.function(on_episode_start),
+ "on_episode_end": tune.function(on_episode_end)
+ }
def logger_creator(conf):
"""Creates a Unified logger with a default logdir prefix
@@ -172,7 +204,7 @@ def train(config, reporter):
def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
map_width, map_height, horizon, policy_folder_name, local_dir, obs_builder,
entropy_coeff, seed, conv_model, rail_generator, nr_extra, kl_coeff, lambda_gae,
- predictor, step_memory):
+ step_memory, min_dist):
tune.run(
train,
name=name,
@@ -193,12 +225,13 @@ def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
"nr_extra": nr_extra,
"kl_coeff": kl_coeff,
"lambda_gae": lambda_gae,
- "predictor": predictor,
+ "min_dist": min_dist,
+ # "predictor": predictor,
"step_memory": step_memory
},
resources_per_trial={
- "cpu": 5,
- "gpu": 0.2
+ "cpu": 8,
+ "gpu": 0
},
verbose=2,
local_dir=local_dir
@@ -207,8 +240,9 @@ def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
if __name__ == '__main__':
gin.external_configurable(tune.grid_search)
- with path('RLLib_training.experiment_configs.experiment_agent_memory', 'config.gin') as f:
- gin.parse_config_file(f)
-
- dir = os.path.join(__file_dirname__, 'experiment_configs', 'experiment_agent_memory')
+ # with path('RLLib_training.experiment_configs.n_agents_experiment', 'config.gin') as f:
+ # gin.parse_config_file(f)
+ gin.parse_config_file('/home/guillaume/flatland/baselines/RLLib_training/experiment_configs/score_metric_test/config.gin')
+ dir = '/home/guillaume/flatland/baselines/RLLib_training/experiment_configs/score_metric_test'
+ # dir = os.path.join(__file_dirname__, 'experiment_configs', 'experiment_agent_memory')
run_experiment(local_dir=dir)