def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
np.random.seed(SEED)
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(SEED)
env = NormalizedBoxEnv(
CentauroTrayEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
if variant['log_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
raise NotImplementedError
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size]
)
policy = TanhMlpPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
es = OUStrategy(
action_space=env.action_space,
mu=0,
theta=0.15,
max_sigma=0.3,
min_sigma=0.3,
decay_period=100000,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
# Clamp model parameters
qf.clamp_all_params(min=-0.003, max=0.003)
policy.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = DDPG(
explo_env=env,
policy=policy,
explo_policy=exploration_policy,
qf=qf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
np.random.seed(SEED)
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(SEED)
goal = variant['env_params'].get('goal')
variant['env_params']['goal_poses'] = \
[goal, (goal[0], 'any'), ('any', goal[1])]
variant['env_params'].pop('goal')
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
if variant['log_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
qf = data['qf']
policy = data['policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size]
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
# Clamp model parameters
qf.clamp_all_params(min=-0.003, max=0.003)
policy.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_replay_buffer_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = PPO(
env=env,
policy=policy,
qf=qf,
# replay_buffer=replay_buffer,
# batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm