| 98 | } |
| 99 | |
| 100 | def _build_full_config(seed: int): |
| 101 | cfg_path = ( |
| 102 | REPO_ROOT |
| 103 | / "source" |
| 104 | / "BrainCo_DexHand" |
| 105 | / "BrainCo_DexHand" |
| 106 | / "tasks" |
| 107 | / "direct" |
| 108 | / "hora_rotation" |
| 109 | / "agents" |
| 110 | / f"{args.train_cfg}.yaml" |
| 111 | ) |
| 112 | train_cfg = OmegaConf.load(str(cfg_path)) |
| 113 | train_cfg.algo = args.algo |
| 114 | train_cfg.load_path = os.path.abspath(args.checkpoint) if args.checkpoint else '' |
| 115 | train_cfg.ppo.output_name = args.output_name |
| 116 | minibatch = train_cfg.ppo.minibatch_size |
| 117 | min_envs = minibatch // train_cfg.ppo.horizon_length |
| 118 | if not args.test and (args.num_envs < min_envs or (args.num_envs * train_cfg.ppo.horizon_length) % minibatch != 0): |
| 119 | raise ValueError( |
| 120 | f"num_envs ({args.num_envs}) must be >= {min_envs} and num_envs*horizon must be divisible " |
| 121 | f"by minibatch_size ({minibatch}). Valid num_envs: {', '.join(str(i) for i in range(min_envs, 20000, min_envs))}..." |
| 122 | ) |
| 123 | train_cfg.ppo.num_actors = args.num_envs |
| 124 | train_cfg.ppo.priv_info = True |
| 125 | train_cfg.ppo.proprio_adapt = args.algo == 'ProprioAdapt' |
| 126 | |
| 127 | rl_device = getattr(args, 'device', None) or 'cuda:0' |
| 128 | return OmegaConf.create({ |
| 129 | 'rl_device': rl_device, |
| 130 | 'test': args.test, |
| 131 | 'seed': seed, |
| 132 | 'train': train_cfg, |
| 133 | }) |
| 134 | |
| 135 | |
| 136 | def _build_env_cfg(seed: int): |