diff --git a/flatland/core/env_observation_builder.py b/flatland/core/env_observation_builder.py
index 53e7a068b73f9907217777251bce0fdd704603be..060785f537251484ab4fd1c520fc92bc8a564cbc 100644
--- a/flatland/core/env_observation_builder.py
+++ b/flatland/core/env_observation_builder.py
@@ -73,3 +73,24 @@ class ObservationBuilder:
direction = np.zeros(4)
direction[agent.direction] = 1
return direction
+
+class DummyObservationBuilder(ObservationBuilder):
+ """
+ DummyObservationBuilder class which returns dummy observations
+ This is used in the evaluation service
+ """
+
+ def __init__(self):
+ self.observation_space = ()
+
+ def _set_env(self, env):
+ self.env = env
+
+ def reset(self):
+ pass
+
+ def get_many(self, handles=[]):
+ return True
+
+ def get(self, handle=0):
+ return True
diff --git a/flatland/evaluators/client.py b/flatland/evaluators/client.py
index 0853f498705d8322cbdfcd223c701e4a587aedf0..e8a8e623c8198698a5f074dc773b0a8cc8f2b69c 100644
--- a/flatland/evaluators/client.py
+++ b/flatland/evaluators/client.py
@@ -18,6 +18,14 @@ logger.setLevel(logging.INFO)
m.patch()
+def are_dicts_equal(d1, d2):
+ """ return True if all keys and values are the same """
+ return all(k in d2 and d1[k] == d2[k]
+ for k in d1) \
+ and all(k in d1 and d1[k] == d2[k]
+ for k in d2)
+
+
class FlatlandRemoteClient(object):
"""
Redis client to interface with flatland-rl remote-evaluation-service
@@ -133,10 +141,16 @@ class FlatlandRemoteClient(object):
else:
return True
- def env_create(self, params={}):
+ def env_create(self, obs_builder_object):
+ """
+ Create a local env and remote env on which the
+ local agent can operate.
+ The observation builder is only used in the local env
+ and the remote env uses a DummyObservationBuilder
+ """
_request = {}
_request['type'] = messages.FLATLAND_RL.ENV_CREATE
- _request['payload'] = params
+ _request['payload'] = {}
_response = self._blocking_request(_request)
observation = _response['payload']['observation']
@@ -151,17 +165,15 @@ class FlatlandRemoteClient(object):
width=1,
height=1,
rail_generator=rail_from_file(test_env_file_path),
- obs_builder_object=TreeObsForRailEnv(
- max_depth=3,
- predictor=ShortestPathPredictorForRailEnv()
- )
+ obs_builder_object=obs_builder_object
)
self.env._max_episode_steps = \
int(1.5 * (self.env.width + self.env.height))
- self.env.reset()
- # Use the observation from the remote service instead
- return observation
+ local_observation = self.env.reset()
+ # Use the local observation
+ # as the remote server uses a dummy observation builder
+ return local_observation
def env_step(self, action, render=False):
"""
@@ -173,11 +185,25 @@ class FlatlandRemoteClient(object):
_request['payload']['action'] = action
_response = self._blocking_request(_request)
_payload = _response['payload']
- observation = _payload['observation']
+
+ # remote_observation = _payload['observation']
reward = _payload['reward']
done = _payload['done']
info = _payload['info']
- return [observation, reward, done, info]
+
+ # Replicate the action in the local env
+ local_observation, local_rewards, local_done, local_info = \
+ self.env.step(action)
+
+ assert are_dicts_equal(reward, local_rewards)
+ assert are_dicts_equal(done, local_done)
+
+ # Return local_observation instead of remote_observation
+ # as the remote_observation is build using a dummy observation
+ # builder
+ # We return the remote rewards and done as they are the
+ # once used by the evaluator
+ return [local_observation, reward, done, info]
def submit(self):
_request = {}
@@ -196,28 +222,37 @@ class FlatlandRemoteClient(object):
if __name__ == "__main__":
- env_client = FlatlandRemoteClient()
+ remote_client = FlatlandRemoteClient()
def my_controller(obs, _env):
_action = {}
for _idx, _ in enumerate(_env.agents):
_action[_idx] = np.random.randint(0, 5)
return _action
+
+ my_observation_builder = TreeObsForRailEnv(max_depth=3,
+ predictor=ShortestPathPredictorForRailEnv())
episode = 0
obs = True
- while obs:
- obs = env_client.env_create()
+ while obs:
+ obs = remote_client.env_create(
+ obs_builder_object=my_observation_builder
+ )
if not obs:
+ """
+ The remote env returns False as the first obs
+ when it is done evaluating all the individual episodes
+ """
break
print("Episode : {}".format(episode))
episode += 1
- print(env_client.env.dones['__all__'])
+ print(remote_client.env.dones['__all__'])
while True:
- action = my_controller(obs, env_client.env)
- observation, all_rewards, done, info = env_client.env_step(action)
+ action = my_controller(obs, remote_client.env)
+ observation, all_rewards, done, info = remote_client.env_step(action)
if done['__all__']:
print("Current Episode : ", episode)
print("Episode Done")
@@ -225,6 +260,6 @@ if __name__ == "__main__":
break
print("Evaluation Complete...")
- print(env_client.submit())
+ print(remote_client.submit())
diff --git a/flatland/evaluators/service.py b/flatland/evaluators/service.py
index 1729196ce01cc7507d93f3de20d139e29ce9f06e..3c1fb2e9013896990204e291e239c4de4becc21c 100644
--- a/flatland/evaluators/service.py
+++ b/flatland/evaluators/service.py
@@ -3,8 +3,7 @@ from __future__ import print_function
import redis
from flatland.envs.generators import rail_from_file
from flatland.envs.rail_env import RailEnv
-from flatland.envs.observations import TreeObsForRailEnv
-from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+from flatland.core.env_observation_builder import DummyObservationBuilder
from flatland.evaluators import messages
import numpy as np
import msgpack
@@ -235,7 +234,6 @@ class FlatlandRemoteEvaluationService:
Add a high level summary of everything thats
hapenning here.
"""
- env_params = command["payload"] # noqa F841
if self.simulation_count < len(self.env_file_paths):
"""
@@ -244,19 +242,11 @@ class FlatlandRemoteEvaluationService:
test_env_file_path = self.env_file_paths[self.simulation_count]
del self.env
- # TODO : Use env_params dictionary to instantiate
- # the RailEnv
- # Maybe use a gin-like interface ?
- # Needs discussion with Erik + Giacomo
- # -Mohanty
self.env = RailEnv(
width=1,
height=1,
rail_generator=rail_from_file(test_env_file_path),
- obs_builder_object=TreeObsForRailEnv(
- max_depth=3,
- predictor=ShortestPathPredictorForRailEnv()
- )
+ obs_builder_object=DummyObservationBuilder()
)
# Set max episode steps allowed