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from easydict import EasyDict
import torch.nn as nn
overcooked_ppo_config = dict(
exp_name="overcooked_ppo_seed0",
env=dict(
collector_env_num=8,
evaluator_env_num=10,
n_evaluator_episode=10,
concat_obs=False, # stack 2 agents' obs in channel dim
stop_value=80,
),
policy=dict(
cuda=True,
multi_agent=True,
action_space='discrete',
model=dict(
obs_shape=(26, 5, 4),
action_shape=6,
action_space='discrete',
),
learn=dict(
epoch_per_collect=4,
batch_size=128,
learning_rate=0.0005,
entropy_weight=0.01,
value_norm=True,
),
collect=dict(
n_sample=1024,
discount_factor=0.99,
gae_lambda=0.95,
),
),
)
overcooked_ppo_config = EasyDict(overcooked_ppo_config)
main_config = overcooked_ppo_config
cartpole_ppo_create_config = dict(
env=dict(
type='overcooked_game',
import_names=['dizoo.overcooked.envs.overcooked_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ppo'),
)
cartpole_ppo_create_config = EasyDict(cartpole_ppo_create_config)
create_config = cartpole_ppo_create_config
class OEncoder(nn.Module):
def __init__(self, obs_shape):
super(OEncoder, self).__init__()
self.act = nn.ReLU()
self.main = nn.Sequential(
*[
nn.Conv2d(obs_shape[0], 64, 3, 1, 1),
self.act,
nn.Conv2d(64, 64, 3, 1, 1),
self.act,
nn.Conv2d(64, 64, 3, 1, 1),
self.act,
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
]
)
def forward(self, x):
x = x.float()
B, A = x.shape[:2]
x = x.view(-1, *x.shape[2:])
x = self.main(x)
return x.view(B, A, 64)
if __name__ == "__main__":
from ding.entry import serial_pipeline_onpolicy
from ding.model.template import VAC
m = main_config.policy.model
encoder = OEncoder(obs_shape=m.obs_shape)
model = VAC(obs_shape=m.obs_shape, action_shape=m.action_shape, action_space=m.action_space, encoder=encoder)
serial_pipeline_onpolicy([main_config, create_config], seed=0, model=model)
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