您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
32 行
1.4 KiB
32 行
1.4 KiB
import numpy as np
|
|
from mlagents.trainers.buffer import AgentBuffer
|
|
from mlagents_envs.base_env import BehaviorSpec
|
|
from mlagents.trainers.trajectory import SplitObservations
|
|
|
|
|
|
def create_agent_buffer(
|
|
behavior_spec: BehaviorSpec, number: int, reward: float = 0.0
|
|
) -> AgentBuffer:
|
|
buffer = AgentBuffer()
|
|
curr_observations = [
|
|
np.random.normal(size=shape) for shape in behavior_spec.observation_shapes
|
|
]
|
|
next_observations = [
|
|
np.random.normal(size=shape) for shape in behavior_spec.observation_shapes
|
|
]
|
|
action = behavior_spec.create_random_action(1)[0, :]
|
|
for _ in range(number):
|
|
curr_split_obs = SplitObservations.from_observations(curr_observations)
|
|
next_split_obs = SplitObservations.from_observations(next_observations)
|
|
for i, _ in enumerate(curr_split_obs.visual_observations):
|
|
buffer["visual_obs%d" % i].append(curr_split_obs.visual_observations[i])
|
|
buffer["next_visual_obs%d" % i].append(
|
|
next_split_obs.visual_observations[i]
|
|
)
|
|
buffer["vector_obs"].append(curr_split_obs.vector_observations)
|
|
buffer["next_vector_in"].append(next_split_obs.vector_observations)
|
|
buffer["actions"].append(action)
|
|
buffer["reward"].append(np.ones(1, dtype=np.float32) * reward)
|
|
buffer["masks"].append(np.ones(1, dtype=np.float32))
|
|
buffer["done"] = np.zeros(number, dtype=np.float32)
|
|
return buffer
|