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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
)
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