diff --git a/docs/flatland_2.0.md b/docs/flatland_2.0.md
index 8ec08f98636212718c29614f4ea042fdf6db70ca..05982babbab45e6aa0d819424c317d5ae05bb2ac 100644
--- a/docs/flatland_2.0.md
+++ b/docs/flatland_2.0.md
@@ -189,18 +189,18 @@ 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 agent is entering by checking `info`:
+In your controller, you can check whether an agent requires an action by checking `info`:
```
obs, rew, done, info = env.step(actions)
...
action_dict = dict()
for a in range(env.get_num_agents()):
- if info['entering'][a] && info['malfunction'][a] == 0 &&:
+ if info['action_required'][a] and info['malfunction'][a] == 0:
action_dict.update({a: ...})
```
-Notice that `info['entering'][a]` does not mean that the action will have an effect:
-if the next cell is blocked or the agent is malfunctioning, the action cannot be performed.
+Notice that `info['action_required'][a]` does not mean that the action will have an effect:
+if the next cell is blocked or the agent breaks down, the action cannot be performed and an action will be required again in the next step.
## Rail Generators and Schedule Generators
The separation between rail generator and schedule generator reflects the organisational separation in the railway domain
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index c9889c5762196289697e872e648ec56701064fdd..848ac15aab5f9b0c933751da8f278633bb1077b8 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -314,7 +314,7 @@ class RailEnv(Environment):
if self.dones["__all__"]:
self.rewards_dict = {i: r + global_reward for i, r in self.rewards_dict.items()}
info_dict = {
- 'entering': {i: False for i in range(self.get_num_agents())},
+ 'action_required': {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())}
}
@@ -457,7 +457,7 @@ class RailEnv(Environment):
for k in self.dones.keys():
self.dones[k] = True
- entering_agents = {
+ action_required_agents = {
i: self.agents[i].speed_data['position_fraction'] <= epsilon for i in range(self.get_num_agents())
}
malfunction_agents = {
@@ -466,7 +466,7 @@ class RailEnv(Environment):
speed_agents = {i: self.agents[i].speed_data['speed'] for i in range(self.get_num_agents())}
info_dict = {
- 'entering': entering_agents,
+ 'action_required': action_required_agents,
'malfunction': malfunction_agents,
'speed': speed_agents
}
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index c23593463c2b679cfd09fcbcf390c3d5a05acde4..8573c25c31f59d9ddb3d9341d891aa1eae231b8e 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -629,13 +629,13 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
available_nodes_full = np.delete(available_nodes_full, delete_idx, 0)
# Priority city to intersection connections
- if False and current_node < num_cities and len(available_intersections) > 0:
+ if current_node < num_cities and len(available_intersections) > 0:
available_nodes = available_intersections
delete_idx = np.where(available_cities == current_node)
available_cities = np.delete(available_cities, delete_idx, 0)
# Priority intersection to city connections
- elif False and current_node >= num_cities and len(available_cities) > 0:
+ elif current_node >= num_cities and len(available_cities) > 0:
available_nodes = available_cities
delete_idx = np.where(available_intersections == current_node)
available_intersections = np.delete(available_intersections, delete_idx, 0)
diff --git a/tests/test_flatland_envs_sparse_rail_generator.py b/tests/test_flatland_envs_sparse_rail_generator.py
index a56873d12b44fd6e57a5b08a784f3a0807ced77e..4645d80aadc3eb247d8b60b1c8456fc250d7feb8 100644
--- a/tests/test_flatland_envs_sparse_rail_generator.py
+++ b/tests/test_flatland_envs_sparse_rail_generator.py
@@ -29,7 +29,7 @@ def test_sparse_rail_generator():
# TODO test assertions!
-def test_rail_env_entering_info():
+def test_rail_env_action_required_info():
np.random.seed(0)
speed_ration_map = {1.: 0.25, # Fast passenger train
1. / 2.: 0.25, # Fast freight train
@@ -54,7 +54,7 @@ def test_rail_env_entering_info():
number_of_agents=10,
obs_builder_object=GlobalObsForRailEnv())
np.random.seed(0)
- env_only_if_entering = RailEnv(width=50,
+ env_only_if_action_required = RailEnv(width=50,
height=50,
rail_generator=sparse_rail_generator(num_cities=10, # Number of cities in map
num_intersections=10,
@@ -79,28 +79,28 @@ def test_rail_env_entering_info():
print("step {}".format(step))
action_dict_always_action = dict()
- action_dict_only_if_entering = dict()
+ action_dict_only_if_action_required = dict()
# Chose an action for each agent in the environment
for a in range(env_always_action.get_num_agents()):
action = np.random.choice(np.arange(4))
action_dict_always_action.update({a: action})
- if step == 0 or info_only_if_entering['entering'][a]:
- action_dict_only_if_entering.update({a: action})
+ if step == 0 or info_only_if_action_required['action_required'][a]:
+ action_dict_only_if_action_required.update({a: action})
else:
- print("[{}] not entering {}, speed_data={}".format(step, a, env_always_action.agents[a].speed_data))
+ print("[{}] not action_required {}, speed_data={}".format(step, a, env_always_action.agents[a].speed_data))
obs_always_action, rewards_always_action, done_always_action, info_always_action = env_always_action.step(
action_dict_always_action)
- obs_only_if_entering, rewards_only_if_entering, done_only_if_entering, info_only_if_entering = env_only_if_entering.step(
- action_dict_only_if_entering)
+ obs_only_if_action_required, rewards_only_if_action_required, done_only_if_action_required, info_only_if_action_required = env_only_if_action_required.step(
+ action_dict_only_if_action_required)
for a in range(env_always_action.get_num_agents()):
- assert len(obs_always_action[a]) == len(obs_only_if_entering[a])
+ assert len(obs_always_action[a]) == len(obs_only_if_action_required[a])
for i in range(len(obs_always_action[a])):
- assert np.array_equal(obs_always_action[a][i], obs_only_if_entering[a][i])
- assert np.array_equal(rewards_always_action[a], rewards_only_if_entering[a])
- assert np.array_equal(done_always_action[a], done_only_if_entering[a])
- assert info_always_action['entering'][a] == info_only_if_entering['entering'][a]
+ assert np.array_equal(obs_always_action[a][i], obs_only_if_action_required[a][i])
+ assert np.array_equal(rewards_always_action[a], rewards_only_if_action_required[a])
+ assert np.array_equal(done_always_action[a], done_only_if_action_required[a])
+ assert info_always_action['action_required'][a] == info_only_if_action_required['action_required'][a]
env_renderer.render_env(show=True, show_observations=False, show_predictions=False)