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70 行
2.6 KiB
70 行
2.6 KiB
from mlagents.trainers.brain import BrainInfo, BrainParameters, CameraResolution
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from mlagents.envs.base_env import BatchedStepResult, AgentGroupSpec
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from mlagents.envs.exception import UnityEnvironmentException
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import numpy as np
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from typing import List
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def step_result_to_brain_info(
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step_result: BatchedStepResult,
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group_spec: AgentGroupSpec,
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agent_id_prefix: int = None,
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) -> BrainInfo:
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n_agents = step_result.n_agents()
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vis_obs_indices = []
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vec_obs_indices = []
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for index, observation in enumerate(step_result.obs):
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if len(observation.shape) == 2:
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vec_obs_indices.append(index)
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elif len(observation.shape) == 4:
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vis_obs_indices.append(index)
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else:
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raise UnityEnvironmentException(
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"Invalid input received from the environment, the observation should "
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"either be a vector of float or a PNG image"
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)
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if len(vec_obs_indices) == 0:
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vec_obs = np.zeros((n_agents, 0), dtype=np.float32)
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else:
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vec_obs = np.concatenate([step_result.obs[i] for i in vec_obs_indices], axis=1)
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vis_obs = [step_result.obs[i] for i in vis_obs_indices]
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mask = np.ones((n_agents, np.sum(group_spec.action_size)), dtype=np.float32)
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if group_spec.is_action_discrete():
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mask = np.ones(
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(n_agents, np.sum(group_spec.discrete_action_branches)), dtype=np.float32
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)
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if step_result.action_mask is not None:
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mask = 1 - np.concatenate(step_result.action_mask, axis=1)
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if agent_id_prefix is None:
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agent_ids = [str(ag_id) for ag_id in list(step_result.agent_id)]
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else:
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agent_ids = [f"${agent_id_prefix}-{ag_id}" for ag_id in step_result.agent_id]
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return BrainInfo(
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vis_obs,
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vec_obs,
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list(step_result.reward),
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agent_ids,
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list(step_result.done),
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list(step_result.max_step),
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mask,
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)
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def group_spec_to_brain_parameters(
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name: str, group_spec: AgentGroupSpec
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) -> BrainParameters:
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vec_size = np.sum(
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[shape[0] for shape in group_spec.observation_shapes if len(shape) == 1]
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)
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vis_sizes = [shape for shape in group_spec.observation_shapes if len(shape) == 3]
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cam_res = [CameraResolution(s[0], s[1], s[2]) for s in vis_sizes]
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a_size: List[int] = []
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if group_spec.is_action_discrete():
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a_size += list(group_spec.discrete_action_branches)
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vector_action_space_type = 0
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else:
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a_size += [group_spec.action_size]
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vector_action_space_type = 1
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return BrainParameters(
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name, int(vec_size), cam_res, a_size, [], vector_action_space_type
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)
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