wehub-resource-sync
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
# Vendored from SkillRL (Apache-2.0 License)
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# Original: agent_system/environments/base.py
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# Trimmed to only include what ALFWorld needs.
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from typing import List, Tuple, Dict, Any
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import numpy as np
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from collections import defaultdict
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def to_numpy(data):
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"""Convert data to numpy array."""
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# Lazy-check for torch.Tensor to avoid hard dependency on torch
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_torch_tensor = None
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try:
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import torch
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_torch_tensor = torch.Tensor
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except ImportError:
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pass
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if _torch_tensor is not None and isinstance(data, _torch_tensor):
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data = data.detach().cpu().numpy()
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elif isinstance(data, np.ndarray):
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pass
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elif isinstance(data, (int, float, bool, Tuple, List)):
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data = np.array(data)
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else:
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raise ValueError(f"Unsupported type: {type(data)})")
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return data
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class EnvironmentManagerBase:
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"""Base class for vectorized environment managers.
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Manages a set of parallel environments, handles action projection,
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observation post-processing, and history tracking.
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"""
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def __init__(self, envs, projection_f, config):
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self.envs = envs
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self.projection_f = projection_f
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self.config = config
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def reset(self, kwargs) -> Dict[str, Any]:
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obs, infos = self.envs.reset()
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return {'text': None, 'image': obs, 'anchor': None}, infos
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def step(self, text_actions: List[str]):
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actions, valids = self.projection_f(text_actions)
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next_obs, rewards, dones, infos = self.envs.step(actions)
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next_observations = {
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'text': None,
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'image': next_obs,
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'anchor': None,
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}
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for i, info in enumerate(infos):
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info['is_action_valid'] = to_numpy(valids[i])
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rewards = to_numpy(rewards)
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dones = to_numpy(dones)
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return next_observations, rewards, dones, infos
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def close(self) -> None:
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self.envs.close()
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def success_evaluator(self, *args, **kwargs) -> Dict[str, np.ndarray]:
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total_infos = kwargs['total_infos']
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total_batch_list = kwargs['total_batch_list']
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batch_size = len(total_batch_list)
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success = defaultdict(list)
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for bs in range(batch_size):
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self._process_batch(bs, total_batch_list, total_infos, success)
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assert len(success['success_rate']) == batch_size
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return {key: np.array(value) for key, value in success.items()}
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def _process_batch(self, batch_idx, total_batch_list, total_infos, success):
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for i in reversed(range(len(total_batch_list[batch_idx]))):
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batch_item = total_batch_list[batch_idx][i]
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if batch_item['active_masks']:
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info = total_infos[batch_idx][i]
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won_value = float(info['won'])
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success['success_rate'].append(won_value)
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return
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