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Merged
merged 2 commits into from
May 13, 2020
Merged

Develop better error message for #3953 #3963

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b5f95bb
Making the error for wrong number of agents raise consistently
vincentpierre May 13, 2020
b0f9f44
Better error message for inputs of wrong dimensions
vincentpierre May 13, 2020
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13 changes: 5 additions & 8 deletions gym-unity/gym_unity/envs/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,6 @@ def __init__(
self._env.step()

self.visual_obs = None
self._n_agents = -1

# Save the step result from the last time all Agents requested decisions.
self._previous_decision_step: DecisionSteps = None
Expand Down Expand Up @@ -172,6 +171,7 @@ def step(self, action: List[Any]) -> GymStepResult:

self._env.step()
decision_step, terminal_step = self._env.get_steps(self.name)
self._check_agents(max(len(decision_step), len(terminal_step)))
if len(terminal_step) != 0:
# The agent is done
self.game_over = True
Expand Down Expand Up @@ -264,10 +264,11 @@ def seed(self, seed: Any = None) -> None:
logger.warning("Could not seed environment %s", self.name)
return

def _check_agents(self, n_agents: int) -> None:
if self._n_agents > 1:
@staticmethod
def _check_agents(n_agents: int) -> None:
if n_agents > 1:
raise UnityGymException(
"There can only be one Agent in the environment but {n_agents} were detected."
f"There can only be one Agent in the environment but {n_agents} were detected."
)

@property
Expand All @@ -290,10 +291,6 @@ def action_space(self):
def observation_space(self):
return self._observation_space

@property
def number_agents(self):
return self._n_agents


class ActionFlattener:
"""
Expand Down
6 changes: 3 additions & 3 deletions ml-agents-envs/mlagents_envs/environment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -342,9 +342,9 @@ def set_actions(self, behavior_name: BehaviorName, action: np.ndarray) -> None:
expected_shape = (len(self._env_state[behavior_name][0]), spec.action_size)
if action.shape != expected_shape:
raise UnityActionException(
"The behavior {0} needs an input of dimension {1} but received input of dimension {2}".format(
behavior_name, expected_shape, action.shape
)
"The behavior {0} needs an input of dimension {1} for "
"(<number of agents>, <action size>) but received input of "
"dimension {2}".format(behavior_name, expected_shape, action.shape)
)
if action.dtype != expected_type:
action = action.astype(expected_type)
Expand Down