From ea7a351eb41a28c0d96fa69ee1c3b6f4cc123781 Mon Sep 17 00:00:00 2001
From: u214892 <u214892@sbb.ch>
Date: Mon, 2 Sep 2019 17:03:19 +0200
Subject: [PATCH] #154 actionable agents in info dict
---
docs/flatland_2.0.md | 12 +++
flatland/envs/rail_env.py | 25 ++++--
...est_flatland_envs_sparse_rail_generator.py | 82 +++++++++++++++++++
3 files changed, 112 insertions(+), 7 deletions(-)
diff --git a/docs/flatland_2.0.md b/docs/flatland_2.0.md
index e35251dd..6ce07c90 100644
--- a/docs/flatland_2.0.md
+++ b/docs/flatland_2.0.md
@@ -150,6 +150,18 @@ Because the different speeds are implemented as fractions the agents ability to
- Agents can make observations at any time step. Make sure to dscard 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
+You can check whether an action has an effect in the environment's next step:
+```
+obs, rew, done, info = env.step(actions)
+...
+action_dict = dict()
+for a in range(env.get_num_agents()):
+ if info['actionable_agents'][a]:
+ action_dict.update({a: ...})
+
+```
+Notice that `info['actionable_agents'][a]` does not mean that the action has an effect:
+if the next cell is blocked, the action cannot be performed. If the action is valid, it will be performend, though.
## Example code
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index d59ca7dc..0e412d0c 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -310,7 +310,10 @@ class RailEnv(Environment):
if self.dones["__all__"]:
self.rewards_dict = {i: r + global_reward for i, r in self.rewards_dict.items()}
- return self._get_observations(), self.rewards_dict, self.dones, {}
+ info_dict = {
+ 'actionable_agents': {i: False for i in range(self.get_num_agents())}
+ }
+ return self._get_observations(), self.rewards_dict, self.dones, info_dict
for i_agent in range(self.get_num_agents()):
agent = self.agents[i_agent]
@@ -422,18 +425,17 @@ class RailEnv(Environment):
if agent.speed_data['position_fraction'] >= 1.0:
# Perform stored action to transition to the next cell
-
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 fee and that the agent can move
+ # Check that everything is still free and that the agent can move
if all([new_cell_valid, transition_valid, cell_free]):
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
+ # else:
+ # # If the agent cannot move due to any reason, we set its state to not moving
+ # agent.moving = False
if np.equal(agent.position, agent.target).all():
self.dones[i_agent] = True
@@ -451,7 +453,16 @@ class RailEnv(Environment):
for k in self.dones.keys():
self.dones[k] = True
- return self._get_observations(), self.rewards_dict, self.dones, {}
+ actionable_agents = {i: self.agents[i].speed_data['position_fraction'] <= epsilon \
+ for i in range(self.get_num_agents())
+ }
+ info_dict = {
+ 'actionable_agents': actionable_agents
+ }
+
+ for i, agent in enumerate(self.agents):
+ print(" {}: {}".format(i, agent.position))
+ return self._get_observations(), self.rewards_dict, self.dones, info_dict
def _check_action_on_agent(self, action, agent):
# compute number of possible transitions in the current
diff --git a/tests/test_flatland_envs_sparse_rail_generator.py b/tests/test_flatland_envs_sparse_rail_generator.py
index c60d5062..4f481dba 100644
--- a/tests/test_flatland_envs_sparse_rail_generator.py
+++ b/tests/test_flatland_envs_sparse_rail_generator.py
@@ -1,3 +1,5 @@
+import numpy as np
+
from flatland.envs.observations import GlobalObsForRailEnv
from flatland.envs.rail_env import RailEnv
from flatland.envs.rail_generators import sparse_rail_generator
@@ -24,3 +26,83 @@ def test_sparse_rail_generator():
env_renderer = RenderTool(env, gl="PILSVG", )
env_renderer.render_env(show=True, show_observations=True, show_predictions=False)
env_renderer.gl.save_image("./sparse_generator_false.png")
+ # TODO test assertions!
+
+
+def test_rail_env_actionable():
+ np.random.seed(0)
+ speed_ration_map = {1.: 0.25, # Fast passenger train
+ 1. / 2.: 0.25, # Fast freight train
+ 1. / 3.: 0.25, # Slow commuter train
+ 1. / 4.: 0.25} # Slow freight train
+ env_always_action = 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())
+ np.random.seed(0)
+ env_only_if_actionable = 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())
+ env_renderer = RenderTool(env_always_action, gl="PILSVG", )
+
+ for step in range(100):
+ print("step {}".format(step))
+
+ action_dict_always_action = dict()
+ action_dict_only_if_actionable = 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_actionable['actionable_agents'][a]:
+ action_dict_only_if_actionable.update({a: action})
+ else:
+ print("[{}] not actionable {}, 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_actionable, rewards_only_if_actionable, done_only_if_actionable, info_only_if_actionable = env_only_if_actionable.step(
+ action_dict_only_if_actionable)
+
+ for a in range(env_always_action.get_num_agents()):
+ assert len(obs_always_action[a]) == len(obs_only_if_actionable[a])
+ for i in range(len(obs_always_action[a])):
+ assert np.array_equal(obs_always_action[a][i], obs_only_if_actionable[a][i])
+ assert np.array_equal(rewards_always_action[a], rewards_only_if_actionable[a])
+ assert np.array_equal(done_always_action[a], done_only_if_actionable[a])
+ assert info_always_action['actionable_agents'][a] == info_only_if_actionable['actionable_agents'][a]
+
+ env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
+
+ if done_always_action['__all__']:
+ break
--
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