diff --git a/docs/flatland_2.0.md b/docs/flatland_2.0.md
index fb9277b25636f6de619dc92e550c88ff5f74e0d1..78649e18d3585d7a55d1c5e293cc241be995e31e 100644
--- a/docs/flatland_2.0.md
+++ b/docs/flatland_2.0.md
@@ -165,6 +165,18 @@ The different speed profiles can be generated using the `schedule_generator`, wh
Keep in mind that the *fastest speed* is 1 and all slower speeds must be between 1 and 0.
For the submission scoring you can assume that there will be no more than 5 speed profiles.
+
+
+Later versions of **Flat**land might have varying speeds during episodes. Therefore, we return the agent speeds.
+Notice that we do not guarantee that the speed will be computed at each step, but if not costly we will return it at each step.
+In your controller, you can get the agents' speed from the `info` returned by `step`:
+```
+obs, rew, done, info = env.step(actions)
+...
+for a in range(env.get_num_agents()):
+ speed = info['speed'][a]
+```
+
## Actions and observation with different speed levels
Because the different speeds are implemented as fractions the agents ability to perform actions has been updated.
@@ -177,7 +189,7 @@ This action is then executed when a step to the next cell is valid. For example
- Agents can make observations at any time step. Make sure to discard observations without any information. See this [example](https://gitlab.aicrowd.com/flatland/baselines/blob/master/torch_training/training_navigation.py) for a simple implementation.
- The environment checks if agent is allowed to move to next cell only at the time of the switch to the next cell
-In your controller, you can check whether an action has an effect in the environment's next step:
+In your controller, you can check whether an agent is entering by checking `info`:
```
obs, rew, done, info = env.step(actions)
...
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index b9d925443731c2da5f717bfcc1bd1660539b40fd..323742f7316dbda50ca8835cb0c7831f31cce01a 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -314,6 +314,7 @@ class RailEnv(Environment):
info_dict = {
'entering': {i: False for i in range(self.get_num_agents())},
'malfunction': {i: 0 for i in range(self.get_num_agents())},
+ 'speed': {i: 0 for i in range(self.get_num_agents())}
}
return self._get_observations(), self.rewards_dict, self.dones, info_dict
@@ -426,18 +427,17 @@ class RailEnv(Environment):
if agent.speed_data['position_fraction'] >= 1.0:
- # Perform stored action to transition to the next cell
+ # Perform stored action to transition to the next cell as soon as cell is free
cell_free, new_cell_valid, new_direction, new_position, transition_valid = \
self._check_action_on_agent(agent.speed_data['transition_action_on_cellexit'], agent)
- # Check that everything is still free and that the agent can move
- if all([new_cell_valid, transition_valid, cell_free]):
+ if all([new_cell_valid, transition_valid, cell_free]) and agent.malfunction_data['malfunction'] == 0:
agent.position = new_position
agent.direction = new_direction
agent.speed_data['position_fraction'] = 0.0
- # else:
- # # If the agent cannot move due to any reason, we set its state to not moving
- # agent.moving = False
+ elif not transition_valid or not new_cell_valid:
+ # If the agent cannot move due to an invalid transition, we set its state to not moving
+ agent.moving = False
if np.equal(agent.position, agent.target).all():
self.dones[i_agent] = True
@@ -455,16 +455,18 @@ class RailEnv(Environment):
for k in self.dones.keys():
self.dones[k] = True
- entering_agents = {i: self.agents[i].speed_data['position_fraction'] <= epsilon \
- for i in range(self.get_num_agents())
- }
- malfunction_agents = {i: self.agents[i].malfunction_data['malfunction'] \
- for i in range(self.get_num_agents())
- }
+ entering_agents = {
+ i: self.agents[i].speed_data['position_fraction'] <= epsilon for i in range(self.get_num_agents())
+ }
+ malfunction_agents = {
+ i: self.agents[i].malfunction_data['malfunction'] for i in range(self.get_num_agents())
+ }
+ speed_agents = {i: self.agents[i].speed_data['speed'] for i in range(self.get_num_agents())}
info_dict = {
'entering': entering_agents,
- 'malfunction': malfunction_agents
+ 'malfunction': malfunction_agents,
+ 'speed': speed_agents
}
return self._get_observations(), self.rewards_dict, self.dones, info_dict
diff --git a/tests/test_flatland_envs_sparse_rail_generator.py b/tests/test_flatland_envs_sparse_rail_generator.py
index 3aa9ea58f1db705c2a355910f70c14c44b58ff8d..a56873d12b44fd6e57a5b08a784f3a0807ced77e 100644
--- a/tests/test_flatland_envs_sparse_rail_generator.py
+++ b/tests/test_flatland_envs_sparse_rail_generator.py
@@ -55,24 +55,24 @@ def test_rail_env_entering_info():
obs_builder_object=GlobalObsForRailEnv())
np.random.seed(0)
env_only_if_entering = RailEnv(width=50,
- height=50,
- rail_generator=sparse_rail_generator(num_cities=10, # Number of cities in map
- num_intersections=10,
- # Number of interesections in map
- num_trainstations=50,
- # Number of possible start/targets on map
- min_node_dist=6, # Minimal distance of nodes
- node_radius=3,
- # Proximity of stations to city center
- num_neighb=3,
- # Number of connections to other cities
- seed=5, # Random seed
- grid_mode=False
- # Ordered distribution of nodes
- ),
- schedule_generator=sparse_schedule_generator(speed_ration_map),
- number_of_agents=10,
- obs_builder_object=GlobalObsForRailEnv())
+ height=50,
+ rail_generator=sparse_rail_generator(num_cities=10, # Number of cities in map
+ num_intersections=10,
+ # Number of interesections in map
+ num_trainstations=50,
+ # Number of possible start/targets on map
+ min_node_dist=6, # Minimal distance of nodes
+ node_radius=3,
+ # Proximity of stations to city center
+ num_neighb=3,
+ # Number of connections to other cities
+ seed=5, # Random seed
+ grid_mode=False
+ # Ordered distribution of nodes
+ ),
+ schedule_generator=sparse_schedule_generator(speed_ration_map),
+ number_of_agents=10,
+ obs_builder_object=GlobalObsForRailEnv())
env_renderer = RenderTool(env_always_action, gl="PILSVG", )
for step in range(100):
@@ -108,7 +108,7 @@ def test_rail_env_entering_info():
break
-def test_rail_env_malfunction_info():
+def test_rail_env_malfunction_speed_info():
np.random.seed(0)
stochastic_data = {'prop_malfunction': 0.5, # Percentage of defective agents
'malfunction_rate': 30, # Rate of malfunction occurence
@@ -149,6 +149,8 @@ def test_rail_env_malfunction_info():
assert 'malfunction' in info
for a in range(env.get_num_agents()):
assert info['malfunction'][a] >= 0
+ assert info['speed'][a] >= 0 and info['speed'][a] <= 1
+ assert info['speed'][a] == env.agents[a].speed_data['speed']
env_renderer.render_env(show=True, show_observations=False, show_predictions=False)