def __init__(
self,
*args,
her_kwargs,
tsac_kwargs,
**kwargs
):
HER.__init__(self, **her_kwargs)
TwinSAC.__init__(self, *args, **kwargs, **tsac_kwargs)
assert isinstance(
self.replay_buffer, RelabelingReplayBuffer
) or isinstance(
self.replay_buffer, ObsDictRelabelingBuffer
)
def __init__(self,
*args,
observation_key='observation',
desired_goal_key='desired_goal',
**kwargs):
HER.__init__(
self,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
TwinSAC.__init__(self, *args, **kwargs)
assert isinstance(self.replay_buffer,
RelabelingReplayBuffer) or isinstance(
self.replay_buffer, ObsDictRelabelingBuffer)
def experiment(variant):
import gym
env = NormalizedBoxEnv(gym.make('HalfCheetah-v2'))
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
net_size = variant['net_size']
qf1 = FlattenMlp(
hidden_sizes=[net_size, net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=[net_size, net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=[net_size, net_size],
input_size=obs_dim,
output_size=1,
)
policy = TanhGaussianPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = TwinSAC(
env=env,
policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['algo_params']
)
algorithm.to(ptu.device)
algorithm.train()
def __init__(self, *args, url_kwargs, tsac_kwargs, **kwargs):
URL.__init__(self, **url_kwargs)
TwinSAC.__init__(self, *args, **kwargs, **tsac_kwargs)
assert isinstance(self.replay_buffer, ObsDictPathReplayBuffer)