def __init__(
self,
env,
qf,
exploration_policy,
ddpg_kwargs,
tdm_kwargs,
base_kwargs,
policy=None,
replay_buffer=None,
):
DDPG.__init__(
self,
env=env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
replay_buffer=replay_buffer,
**ddpg_kwargs,
**base_kwargs
)
super().__init__(**tdm_kwargs)
# Not supporting these in this implementation
assert self.qf_weight_decay == 0
assert self.residual_gradient_weight == 0
def experiment(variant):
env = variant['env_class'](**variant['env_kwargs'])
env = NormalizedBoxEnv(env, **variant['normalize_kwargs'])
if variant['multitask']:
env = MultitaskToFlatEnv(env)
es = OUStrategy(action_space=env.action_space, **variant['ou_kwargs'])
obs_dim = int(env.observation_space.flat_dim)
action_dim = int(env.action_space.flat_dim)
obs_normalizer = TorchFixedNormalizer(obs_dim)
action_normalizer = TorchFixedNormalizer(action_dim)
qf = MlpQf(input_size=obs_dim + action_dim,
output_size=1,
obs_normalizer=obs_normalizer,
action_normalizer=action_normalizer,
**variant['qf_kwargs'])
policy = TanhMlpPolicy(input_size=obs_dim,
output_size=action_dim,
obs_normalizer=obs_normalizer,
**variant['policy_kwargs'])
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
algorithm = DDPG(env,
qf,
policy,
exploration_policy,
obs_normalizer=obs_normalizer,
action_normalizer=action_normalizer,
**variant['algo_kwargs'])
algorithm.train()
def experiment(variant):
env = gym.make(variant['env_id'])
env = NormalizedBoxEnv(env)
es = GaussianStrategy(action_space=env.action_space, )
obs_dim = env.observation_space.low.size
action_dim = env.action_space.low.size
qf = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[128, 128])
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[128, 128],
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
algorithm = DDPG(env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_params'])
algorithm.to(ptu.device)
algorithm.train()
def example(variant):
env = HalfCheetahEnv()
if variant['normalize']:
env = normalize(env)
es = OUStrategy(action_space=env.action_space)
qf = FeedForwardQFunction(
int(env.observation_space.flat_dim),
int(env.action_space.flat_dim),
32,
32,
)
policy = FeedForwardPolicy(
int(env.observation_space.flat_dim),
int(env.action_space.flat_dim),
32,
32,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
algorithm = DDPG(env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_params'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env_params = variant['env_params']
env = SawyerXYZReachingEnv(**env_params)
es = OUStrategy(action_space=env.action_space)
obs_dim = env.observation_space.low.size
action_dim = env.action_space.low.size
qf = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[400, 300],
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[400, 300],
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
algorithm = DDPG(env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
def experiment(variant):
imsize = variant['imsize']
history = variant['history']
#env = InvertedDoublePendulumEnv()#gym.make(variant['env_id'])
# env = SawyerXYZEnv()
env = RandomGoalPusher2DEnv()
partial_obs_size = env.obs_dim
env = NormalizedBoxEnv(
ImageMujocoWithObsEnv(env,
imsize=imsize,
keep_prev=history - 1,
init_camera=variant['init_camera']))
# es = GaussianStrategy(
# action_space=env.action_space,
# )
es = OUStrategy(action_space=env.action_space)
obs_dim = env.observation_space.low.size
action_dim = env.action_space.low.size
qf = MergedCNN(input_width=imsize,
input_height=imsize,
output_size=1,
input_channels=history,
added_fc_input_size=action_dim + partial_obs_size,
**variant['cnn_params'])
policy = CNNPolicy(
input_width=imsize,
input_height=imsize,
added_fc_input_size=partial_obs_size,
output_size=action_dim,
input_channels=history,
**variant['cnn_params'],
output_activation=torch.tanh,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
algorithm = DDPG(
env,
qf=qf,
policy=policy,
# qf_weight_decay=.01,
exploration_policy=exploration_policy,
**variant['algo_params'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env = variant['env_class'](**variant['env_kwargs'])
env = MultitaskToFlatEnv(env)
if variant['normalize']:
env = NormalizedBoxEnv(env)
exploration_type = variant['exploration_type']
if exploration_type == 'ou':
es = OUStrategy(action_space=env.action_space)
elif exploration_type == 'gaussian':
es = GaussianStrategy(
action_space=env.action_space,
max_sigma=0.1,
min_sigma=0.1, # Constant sigma
)
elif exploration_type == 'epsilon':
es = EpsilonGreedy(
action_space=env.action_space,
prob_random_action=0.1,
)
else:
raise Exception("Invalid type: " + exploration_type)
obs_dim = env.observation_space.low.size
action_dim = env.action_space.low.size
qf = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[400, 300],
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[400, 300],
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
algorithm = DDPG(
env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env = NormalizedBoxEnv(
MultiGoalEnv(
actuation_cost_coeff=10,
distance_cost_coeff=1,
goal_reward=10,
))
es = OUStrategy(action_space=env.action_space)
qf = FeedForwardQFunction(
int(env.observation_space.flat_dim),
int(env.action_space.flat_dim),
100,
100,
)
policy = FeedForwardPolicy(
int(env.observation_space.flat_dim),
int(env.action_space.flat_dim),
100,
100,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
plotter = QFPolicyPlotter(
qf=qf,
# policy=policy,
policy=exploration_policy,
obs_lst=np.array([[-2.5, 0.0], [0.0, 0.0], [2.5, 2.5]]),
default_action=[np.nan, np.nan],
n_samples=100)
algorithm = DDPG(env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
render_eval_paths=True,
plotter=plotter,
**variant['algo_params'])
algorithm.to(ptu.device)
algorithm.train()
def example(variant):
env_class = variant['env_class']
env_params = variant['env_params']
env = env_class(**env_params)
normalize(env)
es_class = variant['es_class']
es_params = dict(action_space=env.action_space, **variant['es_params'])
use_gpu = variant['use_gpu']
es = es_class(**es_params)
qf = FeedForwardQFunction(
int(env.observation_space.flat_dim),
int(env.action_space.flat_dim),
100,
100,
)
policy_class = variant['policy_class']
policy_params = dict(
obs_dim=get_dim(env.observation_space),
action_dim=get_dim(env.action_space),
fc1_size=100,
fc2_size=100,
)
policy = policy_class(**policy_params)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
algorithm = DDPG(
env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_params'],
)
if use_gpu and ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
def experiment(variant):
data = joblib.load(GOOD_DDPG_POLICY_PATH)
expert_policy = data['policy']
env = NormalizedBoxEnv(variant['env_class']())
es = OUStrategy(
action_space=env.action_space,
**variant['es_kwargs']
)
obs_dim = env.observation_space.low.size
action_dim = env.action_space.low.size
qf = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[400, 300],
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[400, 300],
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=expert_policy,
)
algorithm = DDPG(
env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_kwargs']
)
if ptu.gpu_enabled():
expert_policy.to(ptu.device)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env_params = variant['env_params']
es_params = variant['es_params']
env = SawyerXYZReachingEnv(**env_params)
es = OUStrategy(action_space=env.action_space, **es_params)
hidden_sizes = variant['hidden_sizes']
obs_dim = env.observation_space.low.size
action_dim = env.action_space.low.size
qf = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[hidden_sizes, hidden_sizes],
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[hidden_sizes, hidden_sizes],
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
if variant['env_params']['relative_pos_control']:
variant['algo_params']['max_path_length'] = 3
variant['algo_params']['num_steps_per_epoch'] = 15
variant['algo_params']['num_steps_per_eval'] = 15
algorithm = DDPG(env,
qf=qf,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
def evaluate(self, epoch):
DDPG.evaluate(self, epoch)
