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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from typing import List, NamedTuple, Optional
import numpy as np
from mlagents.trainers.buffer import (
AgentBuffer,
ObservationKeyPrefix,
AgentBufferKey,
BufferKey,
)
from mlagents_envs.base_env import ActionTuple
from mlagents.trainers.torch.action_log_probs import LogProbsTuple
class AgentExperience(NamedTuple):
obs: List[np.ndarray]
reward: float
done: bool
action: ActionTuple
action_probs: Optional[LogProbsTuple] # TODO rename to action_log_probs
action_mask: np.ndarray
prev_action: np.ndarray
interrupted: bool
memory: Optional[np.ndarray]
class ObsUtil:
@staticmethod
def get_name_at(index: int) -> AgentBufferKey:
"""
returns the name of the observation given the index of the observation
"""
return ObservationKeyPrefix.OBSERVATION, index
@staticmethod
def get_name_at_next(index: int) -> AgentBufferKey:
"""
returns the name of the next observation given the index of the observation
"""
return ObservationKeyPrefix.NEXT_OBSERVATION, 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 Trajectory(NamedTuple):
steps: List[AgentExperience]
next_obs: List[
np.ndarray
] # Observation following the trajectory, for bootstrapping
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):
if step < len(self.steps) - 1:
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])
if exp.memory is not None:
agent_buffer_trajectory[BufferKey.MEMORY].append(exp.memory)
agent_buffer_trajectory[BufferKey.MASKS].append(1.0)
agent_buffer_trajectory[BufferKey.DONE].append(exp.done)
# Adds the log prob and action of continuous/discrete separately
agent_buffer_trajectory[BufferKey.CONTINUOUS_ACTION].append(
exp.action.continuous
)
agent_buffer_trajectory[BufferKey.DISCRETE_ACTION].append(
exp.action.discrete
)
if exp.action_probs is not None:
agent_buffer_trajectory[BufferKey.CONTINUOUS_LOG_PROBS].append(
exp.action_probs.continuous
)
agent_buffer_trajectory[BufferKey.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[BufferKey.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[BufferKey.ACTION_MASK].append(
np.ones(action_shape, dtype=np.float32), padding_value=1
)
agent_buffer_trajectory[BufferKey.PREV_ACTION].append(exp.prev_action)
agent_buffer_trajectory[BufferKey.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