diff --git a/examples/introduction_flatland_2_1.py b/examples/introduction_flatland_2_1.py
index 87451ecef1f7a07c475c02a3be5915a5cb408ce9..4cdf63b0b32d431efabc05ed4d593aa746a44b40 100644
--- a/examples/introduction_flatland_2_1.py
+++ b/examples/introduction_flatland_2_1.py
@@ -1,3 +1,5 @@
+import numpy as np
+
# In Flatland you can use custom observation builders and predicitors
# Observation builders generate the observation needed by the controller
# Preditctors can be used to do short time prediction which can help in avoiding conflicts in the network
@@ -58,9 +60,9 @@ schedule_generator = sparse_schedule_generator(speed_ration_map)
# We can furthermore pass stochastic data to the RailEnv constructor which will allow for stochastic malfunctions
# during an episode.
-stochastic_data = {'malfunction_rate': 5, # Rate of malfunction occurence of single agent
- 'min_duration': 3, # Minimal duration of malfunction
- 'max_duration': 20 # Max duration of malfunction
+stochastic_data = {'malfunction_rate': 100, # Rate of malfunction occurence of single agent
+ 'min_duration': 15, # Minimal duration of malfunction
+ 'max_duration': 50 # Max duration of malfunction
}
# Custom observation builder without predictor
@@ -107,7 +109,7 @@ class RandomAgent:
:param state: input is the observation of the agent
:return: returns an action
"""
- return 2 # np.random.choice(np.arange(self.action_size))
+ return np.random.choice([1, 2, 3, 4]) # [Left, Forward, Right, Stop]
def step(self, memories):
"""
@@ -251,8 +253,8 @@ for step in range(500):
next_obs, all_rewards, done, _ = env.step(action_dict)
- env_renderer.render_env(show=False, show_observations=False, show_predictions=False)
- env_renderer.gl.save_image('./misc/Fames2/flatland_frame_{:04d}.png'.format(step))
+ env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
+ # env_renderer.gl.save_image('./misc/Fames2/flatland_frame_{:04d}.png'.format(step))
frame_step += 1
# Update replay buffer and train agent
for a in range(env.get_num_agents()):
@@ -262,5 +264,4 @@ for step in range(500):
observations = next_obs.copy()
if done['__all__']:
break
-
print('Episode: Steps {}\t Score = {}'.format(step, score))
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 2c322c7f0ce33a5cb2dc0f197d8d8a85e0e04407..f284f3ac38b480d8feb6c0b4944cf8831d2a70d2 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -431,9 +431,20 @@ class RailEnv(Environment):
breaking_agent_idx = self.np_random.choice(self.active_agents)
breaking_agent = self.agents[breaking_agent_idx]
- # Only break agents that are not broken yet
- # TODO: Do we want to guarantee that we have the desired rate or are we happy with lower rates?
- if breaking_agent.malfunction_data['malfunction'] < 1:
+ # We assume that less then half of the active agents should be broken at MOST.
+ # Therefore we only try that many times before ignoring the malfunction
+
+ tries = 0
+ max_tries = 0.5 * len(self.active_agents)
+
+ # Look for a functioning active agent
+ while breaking_agent.malfunction_data['malfunction'] > 0 and tries < max_tries:
+ breaking_agent_idx = self.np_random.choice(self.active_agents)
+ breaking_agent = self.agents[breaking_agent_idx]
+ tries += 1
+
+ # If we did not manage to find a functioning agent among the active ones skip this malfunction
+ if tries < max_tries:
# Because we update agents in the same step as we break them we add one to the duration of the
# malfunction
num_broken_steps = self.np_random.randint(self.min_number_of_steps_broken,
@@ -443,6 +454,10 @@ class RailEnv(Environment):
breaking_agent.malfunction_data['fixed'] = False
breaking_agent.malfunction_data['nr_malfunctions'] += 1
+ return
+
+ return
+
def step(self, action_dict_: Dict[int, RailEnvActions]):
"""
Updates rewards for the agents at a step.
diff --git a/tests/test_flatland_malfunction.py b/tests/test_flatland_malfunction.py
index da710ad375019adbcf2a0bf83f49ded6b457b93c..e18685f6c68f757f50075ebf206423b562514dd6 100644
--- a/tests/test_flatland_malfunction.py
+++ b/tests/test_flatland_malfunction.py
@@ -140,17 +140,17 @@ def test_malfunction_process_statistically():
env.agents[0].target = (0, 0)
# Next line only for test generation
- # agent_malfunction_list = [[] for i in range(10)]
- agent_malfunction_list = [[0, 5, 4, 3, 2, 1, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0],
- [0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1],
- [0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
- [5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],
- [0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0],
- [0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+ #agent_malfunction_list = [[] for i in range(10)]
+ agent_malfunction_list = [[0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1],
+ [0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0],
+ [0, 0, 0, 5, 4, 3, 2, 1, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 5, 4],
+ [0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0],
+ [0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 5, 4, 3, 2],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 5],
+ [0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 5, 4, 3],
+ [0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
for step in range(20):
action_dict: Dict[int, RailEnvActions] = {}
@@ -158,10 +158,10 @@ def test_malfunction_process_statistically():
# We randomly select an action
action_dict[agent_idx] = RailEnvActions(np.random.randint(4))
# For generating tests only:
- # agent_malfunction_list[agent_idx].append(env.agents[agent_idx].malfunction_data['malfunction'])
+ #agent_malfunction_list[agent_idx].append(env.agents[agent_idx].malfunction_data['malfunction'])
assert env.agents[agent_idx].malfunction_data['malfunction'] == agent_malfunction_list[agent_idx][step]
env.step(action_dict)
- # print(agent_malfunction_list)
+ #print(agent_malfunction_list)
def test_malfunction_before_entry():