def test_agentprocessor(num_vis_obs):
policy = create_mock_policy()
tqueue = mock.Mock()
name_behavior_id = "test_brain_name"
processor = AgentProcessor(
policy,
name_behavior_id,
max_trajectory_length=5,
stats_reporter=StatsReporter("testcat"),
)
fake_action_outputs = {
"action": ActionTuple(continuous=np.array([[0.1], [0.1]])),
"entropy": np.array([1.0], dtype=np.float32),
"learning_rate": 1.0,
"log_probs": LogProbsTuple(continuous=np.array([[0.1], [0.1]])),
}
mock_decision_steps, mock_terminal_steps = mb.create_mock_steps(
num_agents=2,
observation_shapes=[(8,)] + num_vis_obs * [(84, 84, 3)],
action_spec=ActionSpec.create_continuous(2),
)
fake_action_info = ActionInfo(
action=ActionTuple(continuous=np.array([[0.1], [0.1]])),
env_action=ActionTuple(continuous=np.array([[0.1], [0.1]])),
value=[0.1, 0.1],
outputs=fake_action_outputs,
agent_ids=mock_decision_steps.agent_id,
)
processor.publish_trajectory_queue(tqueue)
# This is like the initial state after the env reset
processor.add_experiences(
mock_decision_steps, mock_terminal_steps, 0, ActionInfo.empty()
)
for _ in range(5):
processor.add_experiences(
mock_decision_steps, mock_terminal_steps, 0, fake_action_info
)
# Assert that two trajectories have been added to the Trainer
assert len(tqueue.put.call_args_list) == 2
# Assert that the trajectory is of length 5
trajectory = tqueue.put.call_args_list[0][0][0]
assert len(trajectory.steps) == 5
# Assert that the AgentProcessor is empty
assert len(processor.experience_buffers[0]) == 0
# Test empty steps
mock_decision_steps, mock_terminal_steps = mb.create_mock_steps(
num_agents=0,
observation_shapes=[(8,)] + num_vis_obs * [(84, 84, 3)],
action_spec=ActionSpec.create_continuous(2),
)
processor.add_experiences(
mock_decision_steps, mock_terminal_steps, 0, ActionInfo.empty()
)
# Assert that the AgentProcessor is still empty
assert len(processor.experience_buffers[0]) == 0
def test_end_episode():
policy = create_mock_policy()
tqueue = mock.Mock()
name_behavior_id = "test_brain_name"
processor = AgentProcessor(
policy,
name_behavior_id,
max_trajectory_length=5,
stats_reporter=StatsReporter("testcat"),
)
fake_action_outputs = {
"action": ActionTuple(continuous=np.array([[0.1]])),
"entropy": np.array([1.0], dtype=np.float32),
"learning_rate": 1.0,
"log_probs": LogProbsTuple(continuous=np.array([[0.1]])),
}
mock_decision_step, mock_terminal_step = mb.create_mock_steps(
num_agents=1,
observation_shapes=[(8,)],
action_spec=ActionSpec.create_continuous(2),
)
fake_action_info = ActionInfo(
action=ActionTuple(continuous=np.array([[0.1]])),
env_action=ActionTuple(continuous=np.array([[0.1]])),
value=[0.1],
outputs=fake_action_outputs,
agent_ids=mock_decision_step.agent_id,
)
processor.publish_trajectory_queue(tqueue)
# This is like the initial state after the env reset
processor.add_experiences(
mock_decision_step, mock_terminal_step, 0, ActionInfo.empty()
)
# Run 3 trajectories, with different workers (to simulate different agents)
remove_calls = []
for _ep in range(3):
remove_calls.append(mock.call([get_global_agent_id(_ep, 0)]))
for _ in range(5):
processor.add_experiences(
mock_decision_step, mock_terminal_step, _ep, fake_action_info
)
# Make sure we don't add experiences from the prior agents after the done
# Call end episode
processor.end_episode()
# Check that we removed every agent
policy.remove_previous_action.assert_has_calls(remove_calls)
# Check that there are no experiences left
assert len(processor.experience_buffers.keys()) == 0
assert len(processor.last_take_action_outputs.keys()) == 0
assert len(processor.episode_steps.keys()) == 0
assert len(processor.episode_rewards.keys()) == 0
def get_action(
self, decision_requests: DecisionSteps, worker_id: int = 0
) -> ActionInfo:
"""
Decides actions given observations information, and takes them in environment.
:param decision_requests: A dictionary of brain names and DecisionSteps from environment.
:param worker_id: In parallel environment training, the unique id of the environment worker that
the DecisionSteps came from. Used to construct a globally unique id for each agent.
:return: an ActionInfo containing action, memories, values and an object
to be passed to add experiences
"""
if len(decision_requests) == 0:
return ActionInfo.empty()
global_agent_ids = [
get_global_agent_id(worker_id, int(agent_id))
for agent_id in decision_requests.agent_id
] # For 1-D array, the iterator order is correct.
run_out = self.evaluate( # pylint: disable=assignment-from-no-return
decision_requests, global_agent_ids
)
self.save_memories(global_agent_ids, run_out.get("memory_out"))
# For Compatibility with buffer changes for hybrid action support
if "log_probs" in run_out:
log_probs_tuple = LogProbsTuple()
if self.behavior_spec.action_spec.is_continuous():
log_probs_tuple.add_continuous(run_out["log_probs"])
else:
log_probs_tuple.add_discrete(run_out["log_probs"])
run_out["log_probs"] = log_probs_tuple
if "action" in run_out:
action_tuple = ActionTuple()
env_action_tuple = ActionTuple()
if self.behavior_spec.action_spec.is_continuous():
action_tuple.add_continuous(run_out["pre_action"])
env_action_tuple.add_continuous(run_out["action"])
else:
action_tuple.add_discrete(run_out["action"])
env_action_tuple.add_discrete(run_out["action"])
run_out["action"] = action_tuple
run_out["env_action"] = env_action_tuple
self.check_nan_action(run_out.get("action"))
return ActionInfo(
action=run_out.get("action"),
env_action=run_out.get("env_action"),
value=run_out.get("value"),
outputs=run_out,
agent_ids=decision_requests.agent_id,
)
def run(self):
# reset unity environment before start
self.unity_env.reset()
while True:
decision_steps, terminal_steps = self.unity_env.get_steps(
self._behavior_name)
# first process all envs/"agents" in decision steps
actions = []
# process all envs/"agents" that finished their episodes in terminal
# steps
for agent_id in terminal_steps.agent_id:
# first check if a new episode needs to be started
if agent_id not in self.agentID_to_episodeID.keys():
episode_id = self.start_episode()
self.agentID_to_episodeID[agent_id] = episode_id
episode_id = self.agentID_to_episodeID[agent_id]
# get observation, rewards and info
obs = terminal_steps[agent_id].obs
obs = obs[0] if len(obs) == 1 else obs
reward = terminal_steps[agent_id].reward
info = {"interrupted": terminal_steps[agent_id].interrupted}
self.log_returns(episode_id, reward, info)
# end episode and remove agent_id from self.agentID_to_episodeID
self.end_episode(episode_id, obs)
self.agentID_to_episodeID.pop(agent_id)
for agent_id in decision_steps.agent_id:
# first check if a new episode needs to be started
if agent_id not in self.agentID_to_episodeID.keys():
episode_id = self.start_episode()
self.agentID_to_episodeID[agent_id] = episode_id
episode_id = self.agentID_to_episodeID[agent_id]
# get observation and reward and request action
obs = decision_steps[agent_id].obs
obs = obs[0] if len(obs) == 1 else obs
reward = decision_steps[agent_id].reward
# log reward and request action
self.log_returns(episode_id, reward)
actions.append(self.get_action(episode_id, obs))
# set actions in Unity environment
if actions:
if actions[0].dtype == np.float32:
action_tuple = ActionTuple(continuous=np.array(actions))
else:
action_tuple = ActionTuple(discrete=np.array(actions))
self.unity_env.set_actions(self._behavior_name, action_tuple)
self.unity_env.step()
def evaluate_population(pop, env, net_shapes, inference=False):
"""
Evaluation of a whole population
"""
behavior_name = list(env.behavior_specs)[0]
spec = env.behavior_specs[behavior_name]
action_spec = spec.action_spec
fitnesses = [0.] * len(pop)
env.reset()
decision_steps, terminal_steps = env.get_steps(behavior_name)
# Play in an environment for number of steps
episode_length = 1000
while episode_length > 1:
for agent_id in terminal_steps.agent_id:
fitnesses[agent_id] += terminal_steps[agent_id].reward
if len(terminal_steps) == len(pop):
break
# Generate an action for all agents
actions = np.empty( (len(decision_steps), action_spec.continuous_size) )
for agent_id in decision_steps.agent_id:
state = decision_steps[agent_id].obs[0]
fitnesses[agent_id] += decision_steps[agent_id].reward
individual = params_reshape(net_shapes, pop[agent_id])
if action_spec.is_discrete():
action_discrete_probs = get_action(individual, state) # returns probability for each action
actions_discrete = np.argmax(action_discrete_probs) # choose action with highest probability
actions[agent_id] = actions_discrete
elif action_spec.is_continuous():
action_continuous_probs = get_action(individual, state, continuous=True) # returns probability for each action
actions[agent_id] = action_continuous_probs
if episode_length % 251 == 0 and not inference:
env.reset() # reset to change initial position from time to time
else:
if action_spec.is_discrete():
action = ActionTuple(discrete=actions)
elif action_spec.is_continuous():
action = ActionTuple(continuous=actions)
env.set_actions(behavior_name, action)
env.step()
decision_steps, terminal_steps = env.get_steps(behavior_name)
episode_length -= 1
return fitnesses
def step(self, action: List[Any]) -> GymStepResult:
"""Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling `reset()`
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
Args:
action (object/list): an action provided by the environment
Returns:
observation (object/list): agent's observation of the current environment
reward (float/list) : amount of reward returned after previous action
done (boolean/list): whether the episode has ended.
info (dict): contains auxiliary diagnostic information.
"""
if self._flattener is not None:
# Translate action into list
action = self._flattener.lookup_action(action)
action = np.array(action).reshape((1, self.action_size))
action_tuple = ActionTuple()
if self.group_spec.action_spec.is_continuous():
action_tuple.add_continuous(action)
else:
action_tuple.add_discrete(action)
self._env.set_actions(self.name, action_tuple)
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
return self._single_step(terminal_step)
else:
return self._single_step(decision_step)
def test_step(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(discrete_action=False,
visual_inputs=0)
env = UnityEnvironment(" ")
spec = env.behavior_specs["RealFakeBrain"]
env.step()
decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
n_agents = len(decision_steps)
env.set_actions("RealFakeBrain", spec.action_spec.empty_action(n_agents))
env.step()
with pytest.raises(UnityActionException):
env.set_actions("RealFakeBrain",
spec.action_spec.empty_action(n_agents - 1))
decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
n_agents = len(decision_steps)
_empty_act = spec.action_spec.empty_action(n_agents)
next_action = ActionTuple(_empty_act.continuous - 1,
_empty_act.discrete - 1)
env.set_actions("RealFakeBrain", next_action)
env.step()
env.close()
assert isinstance(decision_steps, DecisionSteps)
assert isinstance(terminal_steps, TerminalSteps)
assert len(spec.observation_specs) == len(decision_steps.obs)
assert len(spec.observation_specs) == len(terminal_steps.obs)
for spec, obs in zip(spec.observation_specs, decision_steps.obs):
assert (n_agents, ) + spec.shape == obs.shape
assert 0 in decision_steps
assert 2 in terminal_steps
def solve(self) -> None:
self.reset()
for _ in range(self.n_demos):
for name in self.names:
if self.action_spec.discrete_size > 0:
self.action[name] = ActionTuple(
np.array([], dtype=np.float32),
np.array([[1]] if self.goal[name] > 0 else [[0]],
dtype=np.int32),
)
else:
self.action[name] = ActionTuple(
np.array([[float(self.goal[name])]], dtype=np.float32),
np.array([], dtype=np.int32),
)
self.step()
def _create_action_info(num_agents: int, agent_ids: List[str]) -> ActionInfo:
fake_action_outputs = {
"action": ActionTuple(
continuous=np.array([[0.1]] * num_agents, dtype=np.float32)
),
"entropy": np.array([1.0], dtype=np.float32),
"learning_rate": 1.0,
"log_probs": LogProbsTuple(
continuous=np.array([[0.1]] * num_agents, dtype=np.float32)
),
}
fake_action_info = ActionInfo(
action=ActionTuple(continuous=np.array([[0.1]] * num_agents, dtype=np.float32)),
env_action=ActionTuple(
continuous=np.array([[0.1]] * num_agents, dtype=np.float32)
),
outputs=fake_action_outputs,
agent_ids=agent_ids,
)
return fake_action_info
def ConvertList2DiscreteAction(self, arr, behavior_name):
'''
input data type = list or 1D array -> ex)[3]
!!! Don't Input 2D Array or list like [(0, 2)]
output data type = Actiontuple
'''
actionList = []
actionList.append(arr)
_discrete = np.array(actionList, dtype=np.int32)
action = ActionTuple(discrete=_discrete)
return action
def step(self, action):
# Reshape to (10, 9) as needed for the wrapper
action = action.reshape((10, 9))
act = ActionTuple(action)
self.env.set_actions(self.behavior_name, act)
self.env.step()
decision_steps, terminal_steps = self.env.get_steps(self.behavior_name)
observation, reward = self._decision_to_observation(decision_steps)
done = len(decision_steps) == 0
info = {}
return observation, reward, done, info
def to_action_tuple(self, clip: bool = False) -> ActionTuple:
"""
Returns an ActionTuple
"""
action_tuple = ActionTuple()
if self.continuous_tensor is not None:
_continuous_tensor = self.continuous_tensor
if clip:
_continuous_tensor = torch.clamp(_continuous_tensor, -3, 3) / 3
continuous = ModelUtils.to_numpy(_continuous_tensor)
action_tuple.add_continuous(continuous)
if self.discrete_list is not None:
discrete = ModelUtils.to_numpy(self.discrete_tensor[:, 0, :])
action_tuple.add_discrete(discrete)
return action_tuple
def step(
self, actions: List[np.ndarray]
) -> GymResult: # todo add support for ActionTuple(continuous, discrete)
curr_action_idx = 0
for team in self.team_names:
# print(f'start idx={curr_action_idx}. End idx = {curr_action_idx + self.agent_per_team[team]}')
# print(f'len action list:{len(actions[curr_action_idx:curr_action_idx + self.agent_per_team[team]])}')
action = np.vstack(actions[curr_action_idx:curr_action_idx +
self.agent_per_team[team]])
# print(f'actions shape:{action.shape}')
self._e.set_actions(team, ActionTuple(action))
curr_action_idx += self.agent_per_team[team]
self._e.step()
self.n += 1
return self.collect_obs()
def set_actions(self, behavior_name, action):
# The ActionTuple contains the actions for all n_agents. This
# slices the ActionTuple into an action tuple for each environment
# and sets it. The index j is used to ignore agents that have already
# reached done.
j = 0
for i in range(self.num_agents):
_act = ActionTuple()
name_and_num = behavior_name + str(i)
env = self.envs[name_and_num]
if not self.dones[name_and_num]:
if self.action_spec.continuous_size > 0:
_act.add_continuous(action.continuous[j:j + 1])
if self.action_spec.discrete_size > 0:
_disc_list = [action.discrete[j, :]]
_act.add_discrete(np.array(_disc_list))
j += 1
env.action[behavior_name] = _act
def test_set_action_multi_agent():
engine_config_channel = EngineConfigurationChannel()
env = default_registry[BALL_ID].make(
base_port=6001,
worker_id=0,
no_graphics=True,
side_channels=[engine_config_channel],
)
engine_config_channel.set_configuration_parameters(time_scale=100)
for _ in range(3):
env.reset()
behavior_name = list(env.behavior_specs.keys())[0]
d, t = env.get_steps(behavior_name)
for _ in range(50):
action = np.ones((len(d), 2))
action_tuple = ActionTuple()
action_tuple.add_continuous(action)
env.set_actions(behavior_name, action_tuple)
env.step()
d, t = env.get_steps(behavior_name)
env.close()
def step(self, action):
"""Runs one timestep of the environment"s dynamics.
Once an episode is done, reset() has to be called manually.
Arguments:
action {List} -- A list of at least one discrete action to be executed by the agent
Returns:
{numpy.ndarray} -- Visual observation
{numpy.ndarray} -- Vector observation
{float} -- (Total) Scalar reward signaled by the environment
{bool} -- Whether the episode of the environment terminated
{dict} -- Further episode information (e.g. cumulated reward) retrieved from the environment once an episode completed
"""
# Carry out the agent's action
action_tuple = ActionTuple()
action_tuple.add_discrete(np.asarray(action).reshape([1, -1]))
self._env.set_actions(self._behavior_name, action_tuple)
self._env.step()
info, terminal_info = self._env.get_steps(self._behavior_name)
# Process step results
vis_obs, vec_obs, reward, done = self._process_agent_info(info, terminal_info)
self._rewards.append(reward)
# Record trajectory data
if self._record:
self._trajectory["vis_obs"].append(vis_obs * 255)
self._trajectory["vec_obs"].append(vec_obs)
self._trajectory["rewards"].append(reward)
self._trajectory["actions"].append(action)
# Episode information
if done:
info = {"reward": sum(self._rewards),
"length": len(self._rewards)}
else:
info = None
return vis_obs, vec_obs, reward, done, info
def _add_group_status_and_obs(
self, step: Union[TerminalStep, DecisionStep], worker_id: int
) -> None:
"""
Takes a TerminalStep or DecisionStep and adds the information in it
to self.group_status. This information can then be retrieved
when constructing trajectories to get the status of group mates. Also stores the current
observation into current_group_obs, to be used to get the next group observations
for bootstrapping.
:param step: TerminalStep or DecisionStep
:param worker_id: Worker ID of this particular environment. Used to generate a
global group id.
"""
global_agent_id = get_global_agent_id(worker_id, step.agent_id)
stored_decision_step, idx = self._last_step_result.get(
global_agent_id, (None, None)
)
stored_take_action_outputs = self._last_take_action_outputs.get(
global_agent_id, None
)
if stored_decision_step is not None and stored_take_action_outputs is not None:
# 0, the default group_id, means that the agent doesn't belong to an agent group.
# If 0, don't add any groupmate information.
if step.group_id > 0:
global_group_id = get_global_group_id(worker_id, step.group_id)
stored_actions = stored_take_action_outputs["action"]
action_tuple = ActionTuple(
continuous=stored_actions.continuous[idx],
discrete=stored_actions.discrete[idx],
)
group_status = AgentStatus(
obs=stored_decision_step.obs,
reward=step.reward,
action=action_tuple,
done=isinstance(step, TerminalStep),
)
self._group_status[global_group_id][global_agent_id] = group_status
self._current_group_obs[global_group_id][global_agent_id] = step.obs
def step(self, action: List[Any]) -> GymStepResult:
"""Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling `reset()`
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
Args:
action (object/list): an action provided by the environment
Returns:
observation (object/list): agent's observation of the current environment
reward (float/list) : amount of reward returned after previous action
done (boolean/list): whether the episode has ended.
info (dict): contains auxiliary diagnostic information.
"""
if self._flattener is not None:
# Translate action into list
action = self._flattener.lookup_action(action)
action = np.array(action).reshape((-1, self.action_size))
action_tuple = ActionTuple()
if self.group_spec.action_spec.is_continuous():
action_tuple.add_continuous(action)
else:
action_tuple.add_discrete(action)
self._env.set_actions(self.name, action_tuple)
self._env.step()
decision_step, terminal_step = self._env.get_steps(self.name)
try:
return self.combine_steps(decision_step, terminal_step)
except KeyError:
self.key_error_counter += 1
# print(f"{self.key_error_counter}th KeyError in UnityToMultiGymWrapper. Previous step returned.")
return self.last_stepreturn
def make_fake_trajectory(
length: int,
observation_specs: List[ObservationSpec],
action_spec: ActionSpec,
max_step_complete: bool = False,
memory_size: int = 10,
num_other_agents_in_group: int = 0,
) -> Trajectory:
"""
Makes a fake trajectory of length length. If max_step_complete,
the trajectory is terminated by a max step rather than a done.
"""
steps_list = []
action_size = action_spec.discrete_size + action_spec.continuous_size
for _i in range(length - 1):
obs = []
for obs_spec in observation_specs:
obs.append(np.ones(obs_spec.shape, dtype=np.float32))
reward = 1.0
done = False
action = ActionTuple(
continuous=np.zeros(action_spec.continuous_size, dtype=np.float32),
discrete=np.zeros(action_spec.discrete_size, dtype=np.int32),
)
action_probs = LogProbsTuple(
continuous=np.ones(action_spec.continuous_size, dtype=np.float32),
discrete=np.ones(action_spec.discrete_size, dtype=np.float32),
)
action_mask = (
[
[False for _ in range(branch)]
for branch in action_spec.discrete_branches
] # type: ignore
if action_spec.is_discrete()
else None
)
if action_spec.is_discrete():
prev_action = np.ones(action_size, dtype=np.int32)
else:
prev_action = np.ones(action_size, dtype=np.float32)
max_step = False
memory = np.ones(memory_size, dtype=np.float32)
agent_id = "test_agent"
behavior_id = "test_brain"
group_status = []
for _ in range(num_other_agents_in_group):
group_status.append(AgentStatus(obs, reward, action, done))
experience = AgentExperience(
obs=obs,
reward=reward,
done=done,
action=action,
action_probs=action_probs,
action_mask=action_mask,
prev_action=prev_action,
interrupted=max_step,
memory=memory,
group_status=group_status,
group_reward=0,
)
steps_list.append(experience)
obs = []
for obs_spec in observation_specs:
obs.append(np.ones(obs_spec.shape, dtype=np.float32))
last_experience = AgentExperience(
obs=obs,
reward=reward,
done=not max_step_complete,
action=action,
action_probs=action_probs,
action_mask=action_mask,
prev_action=prev_action,
interrupted=max_step_complete,
memory=memory,
group_status=group_status,
group_reward=0,
)
steps_list.append(last_experience)
return Trajectory(
steps=steps_list,
agent_id=agent_id,
behavior_id=behavior_id,
next_obs=obs,
next_group_obs=[obs] * num_other_agents_in_group,
)
Created on Fri May 7 11:06:32 2021
@author: Win10
"""
import numpy as np
import matplotlib.pyplot as plt
from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.base_env import ActionTuple
# spdyer first, then unity play
print('load_enviroment, prepare to hit play button!')
env = UnityEnvironment(file_name=None, side_channels=[])
# Start interacting with the evironment.
env.reset()
behavior_name = list(env.behavior_specs)[0]
spec = env.behavior_specs[behavior_name]
#vis_obs = any(len(spec.shape) == 3 for spec in spec.observation_specs)
decision_steps, terminal_steps = env.get_steps(behavior_name)
action = ActionTuple(np.array([[1.0, 1.0]], dtype=np.float32))
action = spec.action_spec.random_action(len(decision_steps))
env.set_actions(behavior_name, action)
env.step()
decision_steps2, terminal_steps2 = env.get_steps(behavior_name)
env.close()
def _process_step(
self, step: Union[TerminalStep, DecisionStep], worker_id: int, index: int
) -> None:
terminated = isinstance(step, TerminalStep)
global_agent_id = get_global_agent_id(worker_id, step.agent_id)
global_group_id = get_global_group_id(worker_id, step.group_id)
stored_decision_step, idx = self._last_step_result.get(
global_agent_id, (None, None)
)
stored_take_action_outputs = self._last_take_action_outputs.get(
global_agent_id, None
)
if not terminated:
# Index is needed to grab from last_take_action_outputs
self._last_step_result[global_agent_id] = (step, index)
# This state is the consequence of a past action
if stored_decision_step is not None and stored_take_action_outputs is not None:
obs = stored_decision_step.obs
if self.policy.use_recurrent:
memory = self.policy.retrieve_previous_memories([global_agent_id])[0, :]
else:
memory = None
done = terminated # Since this is an ongoing step
interrupted = step.interrupted if terminated else False
# Add the outputs of the last eval
stored_actions = stored_take_action_outputs["action"]
action_tuple = ActionTuple(
continuous=stored_actions.continuous[idx],
discrete=stored_actions.discrete[idx],
)
stored_action_probs = stored_take_action_outputs["log_probs"]
log_probs_tuple = LogProbsTuple(
continuous=stored_action_probs.continuous[idx],
discrete=stored_action_probs.discrete[idx],
)
action_mask = stored_decision_step.action_mask
prev_action = self.policy.retrieve_previous_action([global_agent_id])[0, :]
# Assemble teammate_obs. If none saved, then it will be an empty list.
group_statuses = []
for _id, _mate_status in self._group_status[global_group_id].items():
if _id != global_agent_id:
group_statuses.append(_mate_status)
experience = AgentExperience(
obs=obs,
reward=step.reward,
done=done,
action=action_tuple,
action_probs=log_probs_tuple,
action_mask=action_mask,
prev_action=prev_action,
interrupted=interrupted,
memory=memory,
group_status=group_statuses,
group_reward=step.group_reward,
)
# Add the value outputs if needed
self._experience_buffers[global_agent_id].append(experience)
self._episode_rewards[global_agent_id] += step.reward
if not terminated:
self._episode_steps[global_agent_id] += 1
# Add a trajectory segment to the buffer if terminal or the length has reached the time horizon
if (
len(self._experience_buffers[global_agent_id])
>= self._max_trajectory_length
or terminated
):
next_obs = step.obs
next_group_obs = []
for _id, _obs in self._current_group_obs[global_group_id].items():
if _id != global_agent_id:
next_group_obs.append(_obs)
trajectory = Trajectory(
steps=self._experience_buffers[global_agent_id],
agent_id=global_agent_id,
next_obs=next_obs,
next_group_obs=next_group_obs,
behavior_id=self._behavior_id,
)
for traj_queue in self._trajectory_queues:
traj_queue.put(trajectory)
self._experience_buffers[global_agent_id] = []
if terminated:
# Record episode length.
self._stats_reporter.add_stat(
"Environment/Episode Length",
self._episode_steps.get(global_agent_id, 0),
)
self._clean_agent_data(global_agent_id)
def stepDiscreteAction(behavior_name, arrlist):
_discrete = np.array(arrlist, dtype=np.int32)
action = ActionTuple(discrete=_discrete)
env.set_actions(behavior_name, action)
env.step()
print("Is there a visual observation ?", vis_obs_bool)
#print action is_discrete
print("Is action is discrete ?", spec.action_spec.is_discrete())
#print action is_continuous
print("Is action is continus ?", spec.action_spec.is_continuous())
#make continuous action and discrete action with 0 array, and step!
decision_steps, terminal_steps = env.get_steps(behavior_name)
n_agents = len(decision_steps)
_continuous = np.zeros((n_agents, spec.action_spec.continuous_size),
dtype=np.float32)
_discrete = np.zeros((n_agents, spec.action_spec.discrete_size),
dtype=np.int32)
action = ActionTuple(continuous=_continuous, discrete=_discrete)
env.set_actions(behavior_name, action)
env.step()
#make custom discrete action, and step!
actionarr = [[1]] #list shape(1<num_agents>,1<discrete_size>)
_discrete = np.array(actionarr, dtype=np.int32)
action = ActionTuple(discrete=_discrete)
env.set_actions(behavior_name, action)
env.step()
#Get step information to get observation
decision_steps, terminal_steps = env.get_steps(behavior_name)
#visual observation
for index, shape in enumerate(spec.observation_shapes):
if len(shape) == 3:
def step(
self, action_dict: MultiAgentDict
) -> Tuple[MultiAgentDict, MultiAgentDict, MultiAgentDict, MultiAgentDict]:
"""Performs one multi-agent step through the game.
Args:
action_dict (dict): Multi-agent action dict with:
keys=agent identifier consisting of
[MLagents behavior name, e.g. "Goalie?team=1"] + "_" +
[Agent index, a unique MLAgent-assigned index per single agent]
Returns:
tuple:
- obs: Multi-agent observation dict.
Only those observations for which to get new actions are
returned.
- rewards: Rewards dict matching `obs`.
- dones: Done dict with only an __all__ multi-agent entry in
it. __all__=True, if episode is done for all agents.
- infos: An (empty) info dict.
"""
from mlagents_envs.base_env import ActionTuple
# Set only the required actions (from the DecisionSteps) in Unity3D.
all_agents = []
for behavior_name in self.unity_env.behavior_specs:
# New ML-Agents API: Set all agents actions at the same time
# via an ActionTuple. Since API v1.4.0.
if self.api_version[0] > 1 or (self.api_version[0] == 1
and self.api_version[1] >= 4):
actions = []
for agent_id in self.unity_env.get_steps(
behavior_name)[0].agent_id:
key = behavior_name + "_{}".format(agent_id)
all_agents.append(key)
actions.append(action_dict[key])
if actions:
if actions[0].dtype == np.float32:
action_tuple = ActionTuple(
continuous=np.array(actions))
else:
action_tuple = ActionTuple(discrete=np.array(actions))
self.unity_env.set_actions(behavior_name, action_tuple)
# Old behavior: Do not use an ActionTuple and set each agent's
# action individually.
else:
for agent_id in self.unity_env.get_steps(
behavior_name)[0].agent_id_to_index.keys():
key = behavior_name + "_{}".format(agent_id)
all_agents.append(key)
self.unity_env.set_action_for_agent(
behavior_name, agent_id, action_dict[key])
# Do the step.
self.unity_env.step()
obs, rewards, dones, infos = self._get_step_results()
# Global horizon reached? -> Return __all__ done=True, so user
# can reset. Set all agents' individual `done` to True as well.
self.episode_timesteps += 1
if self.episode_timesteps > self.episode_horizon:
return obs, rewards, dict({"__all__": True}, **{
agent_id: True
for agent_id in all_agents
}), infos
return obs, rewards, dones, infos