def test_unsquash_action(self):
"""Test to make sure unsquash_action works for both float and int Box spaces."""
space = Box(low=3, high=8, shape=(2, ), dtype=np.float32)
struct = get_base_struct_from_space(space)
action = unsquash_action(0.5, struct)
self.assertEqual(action[0], 6.75)
self.assertEqual(action[1], 6.75)
space = Box(low=3, high=8, shape=(2, ), dtype=np.int32)
struct = get_base_struct_from_space(space)
action = unsquash_action(3, struct)
self.assertEqual(action[0], 6)
self.assertEqual(action[1], 6)
def __call__(self, ac_data: ActionConnectorDataType) -> ActionConnectorDataType:
assert isinstance(
ac_data.output, tuple
), "Action connector requires PolicyOutputType data."
actions, states, fetches = ac_data.output
return ActionConnectorDataType(
ac_data.env_id,
ac_data.agent_id,
(unsquash_action(actions, self._action_space_struct), states, fetches),
)
def _process_policy_eval_results(
*,
to_eval: Dict[PolicyID, List[PolicyEvalData]],
eval_results: Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]],
active_episodes: Dict[str, MultiAgentEpisode],
active_envs: Set[int],
off_policy_actions: MultiEnvDict,
policies: Dict[PolicyID, Policy],
normalize_actions: bool,
clip_actions: bool,
) -> Dict[EnvID, Dict[AgentID, EnvActionType]]:
"""Process the output of policy neural network evaluation.
Records policy evaluation results into the given episode objects and
returns replies to send back to agents in the env.
Args:
to_eval (Dict[PolicyID, List[PolicyEvalData]]): Mapping of policy IDs
to lists of PolicyEvalData objects.
eval_results (Dict[PolicyID, List]): Mapping of policy IDs to list of
actions, rnn-out states, extra-action-fetches dicts.
active_episodes (Dict[str, MultiAgentEpisode]): Mapping from
episode ID to currently ongoing MultiAgentEpisode object.
active_envs (Set[int]): Set of non-terminated env ids.
off_policy_actions (dict): Doubly keyed dict of env-ids -> agent ids ->
off-policy-action, returned by a `BaseEnv.poll()` call.
policies (Dict[PolicyID, Policy]): Mapping from policy ID to Policy.
normalize_actions (bool): Whether to normalize actions to the action
space's bounds.
clip_actions (bool): Whether to clip actions to the action space's
bounds.
Returns:
actions_to_send: Nested dict of env id -> agent id -> actions to be
sent to Env (np.ndarrays).
"""
actions_to_send: Dict[EnvID, Dict[AgentID, EnvActionType]] = \
defaultdict(dict)
# types: int
for env_id in active_envs:
actions_to_send[env_id] = {} # at minimum send empty dict
# types: PolicyID, List[PolicyEvalData]
for policy_id, eval_data in to_eval.items():
actions: TensorStructType = eval_results[policy_id][0]
actions = convert_to_numpy(actions)
rnn_out_cols: StateBatch = eval_results[policy_id][1]
pi_info_cols: dict = eval_results[policy_id][2]
# In case actions is a list (representing the 0th dim of a batch of
# primitive actions), try converting it first.
if isinstance(actions, list):
actions = np.array(actions)
# Store RNN state ins/outs and extra-action fetches to episode.
for f_i, column in enumerate(rnn_out_cols):
pi_info_cols["state_out_{}".format(f_i)] = column
policy: Policy = _get_or_raise(policies, policy_id)
# Split action-component batches into single action rows.
actions: List[EnvActionType] = unbatch(actions)
# types: int, EnvActionType
for i, action in enumerate(actions):
# Normalize, if necessary.
if normalize_actions:
action_to_send = unsquash_action(action,
policy.action_space_struct)
# Clip, if necessary.
elif clip_actions:
action_to_send = clip_action(action,
policy.action_space_struct)
else:
action_to_send = action
env_id: int = eval_data[i].env_id
agent_id: AgentID = eval_data[i].agent_id
episode: MultiAgentEpisode = active_episodes[env_id]
episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols])
episode._set_last_pi_info(
agent_id, {k: v[i]
for k, v in pi_info_cols.items()})
if env_id in off_policy_actions and \
agent_id in off_policy_actions[env_id]:
episode._set_last_action(agent_id,
off_policy_actions[env_id][agent_id])
else:
episode._set_last_action(agent_id, action)
assert agent_id not in actions_to_send[env_id]
actions_to_send[env_id][agent_id] = action_to_send
return actions_to_send
def compute_single_action(
self,
obs: TensorType,
state: Optional[List[TensorType]] = None,
prev_action: Optional[TensorType] = None,
prev_reward: Optional[TensorType] = None,
info: dict = None,
episode: Optional["MultiAgentEpisode"] = None,
clip_actions: bool = None,
explore: Optional[bool] = None,
timestep: Optional[int] = None,
unsquash_actions: bool = None,
**kwargs) -> \
Tuple[TensorType, List[TensorType], Dict[str, TensorType]]:
"""Unbatched version of compute_actions.
Args:
obs (TensorType): Single observation.
state (Optional[List[TensorType]]): List of RNN state inputs, if
any.
prev_action (Optional[TensorType]): Previous action value, if any.
prev_reward (Optional[TensorType]): Previous reward, if any.
info (dict): Info object, if any.
episode (Optional[MultiAgentEpisode]): this provides access to all
of the internal episode state, which may be useful for
model-based or multi-agent algorithms.
unsquash_actions (bool): Should actions be unsquashed according to
the Policy's action space?
clip_actions (bool): Should actions be clipped according to the
Policy's action space?
explore (Optional[bool]): Whether to pick an exploitation or
exploration action
(default: None -> use self.config["explore"]).
timestep (Optional[int]): The current (sampling) time step.
Keyword Args:
kwargs: Forward compatibility.
Returns:
Tuple:
- actions (TensorType): Single action.
- state_outs (List[TensorType]): List of RNN state outputs,
if any.
- info (dict): Dictionary of extra features, if any.
"""
# If policy works in normalized space, we should unsquash the action.
# Use value of config.normalize_actions, if None.
unsquash_actions = \
unsquash_actions if unsquash_actions is not None \
else self.config["normalize_actions"]
clip_actions = clip_actions if clip_actions is not None else \
self.config["clip_actions"]
prev_action_batch = None
prev_reward_batch = None
info_batch = None
episodes = None
state_batch = None
if prev_action is not None:
prev_action_batch = [prev_action]
if prev_reward is not None:
prev_reward_batch = [prev_reward]
if info is not None:
info_batch = [info]
if episode is not None:
episodes = [episode]
if state is not None:
state_batch = [
s.unsqueeze(0) if torch and isinstance(s, torch.Tensor) else
np.expand_dims(s, 0) for s in state
]
out = self.compute_actions([obs],
state_batch,
prev_action_batch=prev_action_batch,
prev_reward_batch=prev_reward_batch,
info_batch=info_batch,
episodes=episodes,
explore=explore,
timestep=timestep)
# Some policies don't return a tuple, but always just a single action.
# E.g. ES and ARS.
if not isinstance(out, tuple):
single_action = out
state_out = []
info = {}
# Normal case: Policy should return (action, state, info) tuple.
else:
batched_action, state_out, info = out
single_action = unbatch(batched_action)
assert len(single_action) == 1
single_action = single_action[0]
# If we work in normalized action space (normalize_actions=True),
# we re-translate here into the env's action space.
if unsquash_actions:
single_action = unsquash_action(single_action,
self.action_space_struct)
# Clip, according to env's action space.
elif clip_actions:
single_action = clip_action(single_action,
self.action_space_struct)
# Return action, internal state(s), infos.
return single_action, [s[0] for s in state_out], \
{k: v[0] for k, v in info.items()}