Spaces:
Sleeping
Sleeping
| from easydict import EasyDict | |
| import torch | |
| import torch.nn as nn | |
| from ding.model.common import FCEncoder, ReparameterizationHead | |
| bipedalwalker_ppo_config = dict( | |
| exp_name='bipedalwalker_ppopg', | |
| env=dict( | |
| env_id='BipedalWalker-v3', | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| # (bool) Scale output action into legal range. | |
| act_scale=True, | |
| n_evaluator_episode=5, | |
| stop_value=500, | |
| rew_clip=True, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| action_space='continuous', | |
| model=dict( | |
| obs_shape=24, | |
| action_shape=4, | |
| ), | |
| learn=dict( | |
| epoch_per_collect=10, | |
| batch_size=64, | |
| learning_rate=3e-4, | |
| entropy_weight=0.0001, | |
| clip_ratio=0.2, | |
| adv_norm=True, | |
| ), | |
| collect=dict( | |
| n_episode=16, | |
| discount_factor=0.99, | |
| collector=dict(get_train_sample=True), | |
| ), | |
| ), | |
| ) | |
| bipedalwalker_ppo_config = EasyDict(bipedalwalker_ppo_config) | |
| main_config = bipedalwalker_ppo_config | |
| bipedalwalker_ppo_create_config = dict( | |
| env=dict( | |
| type='bipedalwalker', | |
| import_names=['dizoo.box2d.bipedalwalker.envs.bipedalwalker_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='ppo_pg'), | |
| collector=dict(type='episode'), | |
| ) | |
| bipedalwalker_ppo_create_config = EasyDict(bipedalwalker_ppo_create_config) | |
| create_config = bipedalwalker_ppo_create_config | |
| class PPOPGContinuousModel(nn.Module): | |
| def __init__(self, obs_shape, action_shape): | |
| super(PPOPGContinuousModel, self).__init__() | |
| self.encoder = nn.Sequential(nn.Linear(obs_shape, 64), nn.Tanh()) | |
| self.head = ReparameterizationHead( | |
| hidden_size=64, | |
| output_size=action_shape, | |
| layer_num=2, | |
| sigma_type='conditioned', | |
| activation=nn.Tanh(), | |
| ) | |
| def forward(self, inputs): | |
| x = self.encoder(inputs) | |
| x = self.head(x) | |
| return {'logit': x} | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_onpolicy -c bipedalwalker_ppo_config.py -s 0` | |
| from ding.entry import serial_pipeline_onpolicy | |
| from copy import deepcopy | |
| for seed in [1, 2, 3]: | |
| new_main_config = deepcopy(main_config) | |
| new_main_config.exp_name += "_seed{}".format(seed) | |
| model = PPOPGContinuousModel(new_main_config.policy.model.obs_shape, new_main_config.policy.model.action_shape) | |
| serial_pipeline_onpolicy( | |
| [new_main_config, deepcopy(create_config)], seed=seed, max_env_step=int(5e6), model=model | |
| ) | |