Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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310 行
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from typing import List, NamedTuple
import itertools
import attr
import numpy as np
from mlagents.trainers.buffer import AgentBuffer
from mlagents_envs.base_env import ActionTuple
from mlagents.trainers.torch.action_log_probs import LogProbsTuple
@attr.s(auto_attribs=True)
class TeammateStatus:
"""
Stores data related to an agent's teammate.
"""
obs: List[np.ndarray]
reward: float
action: ActionTuple
@attr.s(auto_attribs=True)
class AgentExperience:
obs: List[np.ndarray]
teammate_status: List[TeammateStatus]
reward: float
done: bool
action: ActionTuple
action_probs: LogProbsTuple
action_mask: np.ndarray
prev_action: np.ndarray
interrupted: bool
memory: np.ndarray
class ObsUtil:
@staticmethod
def get_name_at(index: int) -> str:
"""
returns the name of the observation given the index of the observation
"""
return f"obs_{index}"
@staticmethod
def get_name_at_next(index: int) -> str:
"""
returns the name of the next observation given the index of the observation
"""
return f"next_obs_{index}"
@staticmethod
def from_buffer(batch: AgentBuffer, num_obs: int) -> List[np.array]:
"""
Creates the list of observations from an AgentBuffer
"""
result: List[np.array] = []
for i in range(num_obs):
result.append(batch[ObsUtil.get_name_at(i)])
return result
@staticmethod
def from_buffer_next(batch: AgentBuffer, num_obs: int) -> List[np.array]:
"""
Creates the list of next observations from an AgentBuffer
"""
result = []
for i in range(num_obs):
result.append(batch[ObsUtil.get_name_at_next(i)])
return result
class TeamObsUtil:
@staticmethod
def get_name_at(index: int) -> str:
"""
returns the name of the observation given the index of the observation
"""
return f"team_obs_{index}"
@staticmethod
def get_name_at_next(index: int) -> str:
"""
returns the name of the next team observation given the index of the observation
"""
return f"team_obs_next_{index}"
@staticmethod
def _padded_time_to_batch(
agent_buffer_field: AgentBuffer.AgentBufferField,
) -> List[np.ndarray]:
"""
Convert an AgentBufferField of List of obs, where one of the dimension is time and the other is number (e.g.
in the case of a variable number of critic observations) to a List of obs, where time is in the batch dimension
of the obs, and the List is the variable number of agents. For cases where there are varying number of agents,
pad the non-existent agents with NaN.
"""
# Find the first observation. This should be USUALLY O(1)
obs_shape = None
for _team_obs in agent_buffer_field:
if _team_obs:
obs_shape = _team_obs[0].shape
break
# If there were no critic obs at all
if obs_shape is None:
return []
new_list = list(
map(
lambda x: np.asanyarray(x),
itertools.zip_longest(
*agent_buffer_field, fillvalue=np.full(obs_shape, np.nan)
),
)
)
return new_list
@staticmethod
def _transpose_list_of_lists(
list_list: List[List[np.ndarray]],
) -> List[List[np.ndarray]]:
return list(map(list, zip(*list_list)))
@staticmethod
def from_buffer(batch: AgentBuffer, num_obs: int) -> List[np.array]:
"""
Creates the list of observations from an AgentBuffer
"""
separated_obs: List[np.array] = []
for i in range(num_obs):
separated_obs.append(
TeamObsUtil._padded_time_to_batch(batch[TeamObsUtil.get_name_at(i)])
)
# separated_obs contains a List(num_obs) of Lists(num_agents), we want to flip
# that and get a List(num_agents) of Lists(num_obs)
result = TeamObsUtil._transpose_list_of_lists(separated_obs)
return result
@staticmethod
def from_buffer_next(batch: AgentBuffer, num_obs: int) -> List[np.array]:
"""
Creates the list of observations from an AgentBuffer
"""
separated_obs: List[np.array] = []
for i in range(num_obs):
separated_obs.append(
TeamObsUtil._padded_time_to_batch(
batch[TeamObsUtil.get_name_at_next(i)]
)
)
# separated_obs contains a List(num_obs) of Lists(num_agents), we want to flip
# that and get a List(num_agents) of Lists(num_obs)
result = TeamObsUtil._transpose_list_of_lists(separated_obs)
return result
class Trajectory(NamedTuple):
steps: List[AgentExperience]
next_obs: List[
np.ndarray
] # Observation following the trajectory, for bootstrapping
next_collab_obs: List[List[np.ndarray]]
agent_id: str
behavior_id: str
def to_agentbuffer(self) -> AgentBuffer:
"""
Converts a Trajectory to an AgentBuffer
:param trajectory: A Trajectory
:returns: AgentBuffer. Note that the length of the AgentBuffer will be one
less than the trajectory, as the next observation need to be populated from the last
step of the trajectory.
"""
agent_buffer_trajectory = AgentBuffer()
obs = self.steps[0].obs
for step, exp in enumerate(self.steps):
is_last_step = step == len(self.steps) - 1
if not is_last_step:
next_obs = self.steps[step + 1].obs
else:
next_obs = self.next_obs
num_obs = len(obs)
for i in range(num_obs):
agent_buffer_trajectory[ObsUtil.get_name_at(i)].append(obs[i])
agent_buffer_trajectory[ObsUtil.get_name_at_next(i)].append(next_obs[i])
# Take care of teammate obs and actions
teammate_continuous_actions, teammate_discrete_actions, teammate_rewards = (
[],
[],
[],
)
for teammate_status in exp.teammate_status:
teammate_rewards.append(teammate_status.reward)
teammate_continuous_actions.append(teammate_status.action.continuous)
teammate_discrete_actions.append(teammate_status.action.discrete)
# Team actions
agent_buffer_trajectory["team_continuous_action"].append(
teammate_continuous_actions
)
agent_buffer_trajectory["team_discrete_action"].append(
teammate_discrete_actions
)
agent_buffer_trajectory["team_rewards"].append(teammate_rewards)
team_reward = teammate_rewards + [exp.reward]
agent_buffer_trajectory["average_team_reward"].append(sum(team_reward)/len(team_reward))
# Next actions
teammate_cont_next_actions = []
teammate_disc_next_actions = []
if not is_last_step:
next_exp = self.steps[step + 1]
for teammate_status in next_exp.teammate_status:
teammate_cont_next_actions.append(teammate_status.action.continuous)
teammate_disc_next_actions.append(teammate_status.action.discrete)
else:
for teammate_status in exp.teammate_status:
teammate_cont_next_actions.append(teammate_status.action.continuous)
teammate_disc_next_actions.append(teammate_status.action.discrete)
agent_buffer_trajectory["team_next_continuous_action"].append(
teammate_cont_next_actions
)
agent_buffer_trajectory["team_next_discrete_action"].append(
teammate_disc_next_actions
)
for i in range(num_obs):
ith_team_obs = []
for _teammate_status in exp.teammate_status:
# Assume teammates have same obs space
ith_team_obs.append(_teammate_status.obs[i])
agent_buffer_trajectory[TeamObsUtil.get_name_at(i)].append(ith_team_obs)
ith_team_obs_next = []
if is_last_step:
for _obs in self.next_collab_obs:
ith_team_obs_next.append(_obs[i])
else:
next_teammate_status = self.steps[step + 1].teammate_status
for _teammate_status in next_teammate_status:
# Assume teammates have same obs space
ith_team_obs_next.append(_teammate_status.obs[i])
agent_buffer_trajectory[TeamObsUtil.get_name_at_next(i)].append(
ith_team_obs_next
)
if exp.memory is not None:
agent_buffer_trajectory["memory"].append(exp.memory)
agent_buffer_trajectory["masks"].append(1.0)
agent_buffer_trajectory["done"].append(exp.done)
# Adds the log prob and action of continuous/discrete separately
agent_buffer_trajectory["continuous_action"].append(exp.action.continuous)
agent_buffer_trajectory["discrete_action"].append(exp.action.discrete)
cont_next_actions = np.zeros_like(exp.action.continuous)
disc_next_actions = np.zeros_like(exp.action.discrete)
if not is_last_step:
next_action = self.steps[step + 1].action
cont_next_actions = next_action.continuous
disc_next_actions = next_action.discrete
agent_buffer_trajectory["next_continuous_action"].append(cont_next_actions)
agent_buffer_trajectory["next_discrete_action"].append(disc_next_actions)
agent_buffer_trajectory["continuous_log_probs"].append(
exp.action_probs.continuous
)
agent_buffer_trajectory["discrete_log_probs"].append(
exp.action_probs.discrete
)
# Store action masks if necessary. Note that 1 means active, while
# in AgentExperience False means active.
if exp.action_mask is not None:
mask = 1 - np.concatenate(exp.action_mask)
agent_buffer_trajectory["action_mask"].append(mask, padding_value=1)
else:
# This should never be needed unless the environment somehow doesn't supply the
# action mask in a discrete space.
action_shape = exp.action.discrete.shape
agent_buffer_trajectory["action_mask"].append(
np.ones(action_shape, dtype=np.float32), padding_value=1
)
agent_buffer_trajectory["prev_action"].append(exp.prev_action)
agent_buffer_trajectory["environment_rewards"].append(exp.reward)
# Store the next visual obs as the current
obs = next_obs
return agent_buffer_trajectory
@property
def done_reached(self) -> bool:
"""
Returns true if trajectory is terminated with a Done.
"""
return self.steps[-1].done
@property
def interrupted(self) -> bool:
"""
Returns true if trajectory was terminated because max steps was reached.
"""
return self.steps[-1].interrupted