def experiment(variant):
num_agent = variant['num_agent']
from cartpole import CartPoleEnv
expl_env = CartPoleEnv(mode=4)
eval_env = CartPoleEnv(mode=4)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
policy_n, qf1_n, target_qf1_n, qf2_n, target_qf2_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], [], []
for i in range(num_agent):
policy = SoftmaxMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
qf1 = FlattenMlp(input_size=(obs_dim * num_agent + action_dim *
(num_agent - 1)),
output_size=action_dim,
**variant['qf_kwargs'])
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(input_size=(obs_dim * num_agent + action_dim *
(num_agent - 1)),
output_size=action_dim,
**variant['qf_kwargs'])
target_qf2 = copy.deepcopy(qf1)
eval_policy = ArgmaxDiscretePolicy(policy)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space),
eval_policy,
)
policy_n.append(policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
trainer = MASACDiscreteTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = gym.make("CartPole-v0")
eval_env = gym.make("CartPole-v0")
obs_dim = expl_env.observation_space.low.size
action_dim = eval_env.action_space.n
qf = Mlp(hidden_sizes=[32, 32], input_size=obs_dim, output_size=action_dim)
target_qf = Mlp(hidden_sizes=[32, 32],
input_size=obs_dim,
output_size=action_dim)
qf_criterion = nn.MSELoss()
eval_policy = ArgmaxDiscretePolicy(qf)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space), eval_policy)
eval_path_collector = MdpPathCollector(eval_env, eval_policy)
expl_path_collector = MdpPathCollector(expl_env, expl_policy)
trainer = DQNTrainer(qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
**variant["trainer_kwargs"])
replay_buffer = EnvReplayBuffer(variant["replay_buffer_size"], expl_env)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
checkpoint_filepath = os.path.join(variant['checkpoint_dir'],
'itr_{}.pkl'.format(
variant['checkpoint_epoch']))
checkpoint = torch.load(checkpoint_filepath)
# the following does not work for Bullet envs yet
# eval_env = checkpoint['evaluation/env']
# expl_env = checkpoint['exploration/env']
eval_env = roboverse.make(variant['env'], transpose_image=True)
expl_env = eval_env
policy = checkpoint['trainer/trainer'].policy
eval_policy = checkpoint['evaluation/policy']
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
policy,
)
observation_key = 'image'
online_buffer_size = 500 * 10 * variant['algorithm_kwargs'][
'max_path_length']
if variant['online_data_only']:
replay_buffer = ObsDictReplayBuffer(online_buffer_size, expl_env,
observation_key=observation_key)
else:
replay_buffer = load_data_from_npy_chaining(
variant, expl_env, observation_key,
extra_buffer_size=online_buffer_size)
trainer_kwargs = variant['trainer_kwargs']
assert trainer_kwargs['min_q_weight'] > 0.
trainer = checkpoint['trainer/trainer']
trainer.min_q_weight = trainer_kwargs['min_q_weight']
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
eval_both=False,
batch_rl=False,
**variant['algorithm_kwargs']
)
video_func = VideoSaveFunction(variant)
algorithm.post_epoch_funcs.append(video_func)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import sys
from traffic.make_env import make_env
expl_env = make_env(args.exp_name)
eval_env = make_env(args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
module = Mlp(
hidden_sizes=[32, 32],
input_size=obs_dim,
output_size=action_dim,
)
policy = SoftmaxPolicy(module, **variant['policy_kwargs'])
qf1 = Mlp(input_size=obs_dim,
output_size=action_dim,
**variant['qf_kwargs'])
target_qf1 = copy.deepcopy(qf1)
qf2 = Mlp(input_size=obs_dim,
output_size=action_dim,
**variant['qf_kwargs'])
target_qf2 = copy.deepcopy(qf2)
eval_policy = ArgmaxDiscretePolicy(policy, use_preactivation=True)
expl_policy = policy
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
expl_policy,
)
qf_criterion = nn.MSELoss()
trainer = SACDiscreteTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
qf_criterion=qf_criterion,
**variant['trainer_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import sys
from traffic.make_env import make_env
expl_env = make_env(args.exp_name)
eval_env = make_env(args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
gb = TrafficGraphBuilder(input_dim=4,
ego_init=torch.tensor([0.,1.]),
other_init=torch.tensor([1.,0.]),
edge_index=torch.tensor([[0,0,1,2],
[1,2,0,0]]))
qf = GNNNet(
pre_graph_builder = gb,
node_dim = 16,
output_dim = action_dim,
post_mlp_kwargs = variant['qf_kwargs'],
num_conv_layers=3)
target_qf = copy.deepcopy(qf)
eval_policy = ArgmaxDiscretePolicy(qf)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space, variant['epsilon']),
eval_policy,
)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
expl_policy,
)
replay_buffer = PrioritizedReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
qf_criterion = nn.MSELoss()
trainer = DQNTrainer(
qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
replay_buffer=replay_buffer,
**variant['trainer_kwargs']
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
'''
1. 建立实验环境（eval, expl）
2. 确立输入，输出维度，建立qf函数，policy函数
3. 复制target qf和 target policy 函数
4. 对于评估构建path collector
5. 对于训练实验，构建探索策略、path collector、replay buffer
6. 构建 DDPGTrainer （qf, policy）
7. algorithm (包括trainer, env, replay buffer, path collector.以及用于评价部分)
8. 开始训练
:param variant: config parameter
:return:
'''
eval_env = NormalizedBoxEnv(HalfCheetahEnv())
expl_env = NormalizedBoxEnv(HalfCheetahEnv())
# Or for a specific version:
# import gym
# env = NormalizedBoxEnv(gym.make('HalfCheetah-v1'))
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
qf = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'])
policy = TanhMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
# 利用copy
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
# 评估
eval_path_collector = MdpPathCollector(eval_env, policy)
# 实验 （探索策略、path收集、replay buffer）
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(action_space=expl_env.action_space),
policy=policy,
)
expl_path_collector = MdpPathCollector(expl_env, exploration_policy)
replay_buffer = EnvReplayBuffer(variant['replay_buffer_size'], expl_env)
trainer = DDPGTrainer(qf=qf,
target_qf=target_qf,
policy=policy,
target_policy=target_policy,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
# 转化变量格式
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env = Point2DEnv(**variant['env_kwargs'])
env = FlatGoalEnv(env)
env = NormalizedBoxEnv(env)
action_dim = int(np.prod(env.action_space.shape))
obs_dim = int(np.prod(env.observation_space.shape))
qf1 = ConcatMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'])
qf2 = ConcatMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'])
target_qf1 = ConcatMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'])
target_qf2 = ConcatMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'])
policy = TanhGaussianPolicy(obs_dim=obs_dim,
action_dim=action_dim,
**variant['policy_kwargs'])
eval_env = expl_env = env
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
policy,
)
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = TwinSACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
data_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
num_agent = variant['num_agent']
from cartpole import CartPoleEnv
expl_env = CartPoleEnv(mode=3)
eval_env = CartPoleEnv(mode=3)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
policy_n, eval_policy_n, qf1_n, target_qf1_n, qf2_n, target_qf2_n = \
[], [], [], [], [], []
for i in range(num_agent):
policy = TanhGaussianPolicy(obs_dim=obs_dim,
action_dim=action_dim,
**variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
qf1 = FlattenMlp(input_size=(obs_dim * num_agent +
action_dim * num_agent),
output_size=1,
**variant['qf_kwargs'])
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(input_size=(obs_dim * num_agent +
action_dim * num_agent),
output_size=1,
**variant['qf_kwargs'])
target_qf2 = copy.deepcopy(qf1)
policy_n.append(policy)
eval_policy_n.append(eval_policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
trainer = MASACTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
qf = CNN(
input_width=obs_dim,
input_height=obs_dim,
input_channels=channels,
output_size=action_dim,
kernel_sizes=[8, 4],
n_channels=[16, 32],
strides=[4, 2],
paddings=[0, 0],
hidden_sizes=[256],
)
target_qf = CNN(
input_width=obs_dim,
input_height=obs_dim,
input_channels=channels,
output_size=action_dim,
kernel_sizes=[8, 4],
n_channels=[16, 32],
strides=[4, 2],
paddings=[0, 0],
hidden_sizes=[256],
)
qf_criterion = nn.MSELoss()
eval_learner_policy = ArgmaxDiscretePolicy(qf)
expl_learner_policy = PolicyWrappedWithExplorationStrategy(
AnnealedEpsilonGreedy(symbolic_action_space,
anneal_rate=variant["anneal_rate"]),
eval_learner_policy,
)
eval_policy = LearnPlanPolicy(eval_learner_policy)
expl_policy = LearnPlanPolicy(expl_learner_policy)
eval_path_collector = MdpPathCollector(eval_env,
eval_policy,
rollout=hierarchical_rollout)
expl_path_collector = MdpPathCollector(expl_env,
expl_policy,
rollout=hierarchical_rollout)
trainer = DQNTrainer(qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
**variant["trainer_kwargs"])
replay_buffer = EnvReplayBuffer(variant["replay_buffer_size"], symb_env)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = gym.make('GoalGridworld-v0')
eval_env = gym.make('GoalGridworld-v0')
obs_dim = expl_env.observation_space.spaces['observation'].low.size
goal_dim = expl_env.observation_space.spaces['desired_goal'].low.size
action_dim = expl_env.action_space.n
qf = FlattenMlp(
input_size=obs_dim + goal_dim,
output_size=action_dim,
hidden_sizes=[400, 300],
)
target_qf = FlattenMlp(
input_size=obs_dim + goal_dim,
output_size=action_dim,
hidden_sizes=[400, 300],
)
eval_policy = ArgmaxDiscretePolicy(qf)
exploration_strategy = EpsilonGreedy(action_space=expl_env.action_space, )
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=exploration_strategy,
policy=eval_policy,
)
replay_buffer = ObsDictRelabelingBuffer(env=eval_env,
**variant['replay_buffer_kwargs'])
observation_key = 'observation'
desired_goal_key = 'desired_goal'
eval_path_collector = GoalConditionedPathCollector(
eval_env,
eval_policy,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = GoalConditionedPathCollector(
expl_env,
expl_policy,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
trainer = DQNTrainer(qf=qf,
target_qf=target_qf,
**variant['trainer_kwargs'])
trainer = HERTrainer(trainer)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = get_env()
eval_env = get_env()
post_epoch_funcs = []
M = variant['layer_size']
trainer = get_sac_model(env=eval_env, hidden_sizes=[M, M])
policy = trainer.policy
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
policy,
)
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
columns = ['Epoch', 'mean', 'std']
eval_result = pd.DataFrame(columns=columns)
eval_output_csv = os.path.join(variant['log_dir'], 'eval_result.csv')
def post_epoch_func(self, epoch):
nonlocal eval_result
nonlocal policy
print(f'-------------post_epoch_func start-------------')
eval_result = my_eval_policy(
env=get_env(),
algorithm=self,
epoch=epoch,
eval_result=eval_result,
output_csv=eval_output_csv,
)
print(f'-------------post_epoch_func done-------------')
algorithm.post_epoch_funcs = [
post_epoch_func,
]
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
# Select a different success_function for different tasks.
expl_env = GymCraftingEnv(state_obs=True,
few_obj=True,
success_function=eval_eatbread)
eval_env = GymCraftingEnv(state_obs=True,
few_obj=True,
success_function=eval_eatbread)
obs_dim = expl_env.observation_space.low.size
action_dim = eval_env.action_space.n
qf = Mlp(
hidden_sizes=[32, 32],
input_size=obs_dim,
output_size=action_dim,
)
target_qf = Mlp(
hidden_sizes=[32, 32],
input_size=obs_dim,
output_size=action_dim,
)
qf_criterion = nn.MSELoss()
eval_policy = ArgmaxDiscretePolicy(qf)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space),
eval_policy,
)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
expl_policy,
)
trainer = DQNTrainer(qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
**variant['trainer_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
args = getArgs()
# expl_env = NormalizedBoxEnv(environment(args))
expl_env = environment(args, 'dqn')
eval_env = environment(args, 'dqn')
# expl_env.render()
obs_dim = expl_env.get_obsdim()
action_dim = expl_env.action_space.n
qf = Mlp(
hidden_sizes=[32, 32],
input_size=obs_dim,
output_size=action_dim,
)
target_qf = Mlp(
hidden_sizes=[32, 32],
input_size=obs_dim,
output_size=action_dim,
)
qf_criterion = nn.MSELoss()
eval_policy = ArgmaxDiscretePolicy(qf)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space),
eval_policy,
)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
expl_policy,
)
trainer = DQNTrainer(qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
**variant['trainer_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def run_sac(base_expl_env, base_eval_env, variant):
expl_env = FlatGoalEnv(base_expl_env, append_goal_to_obs=True)
eval_env = FlatGoalEnv(base_eval_env, append_goal_to_obs=True)
obs_dim = expl_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
M = variant["layer_size"]
num_hidden = variant["num_hidden_layers"]
qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M] * num_hidden)
qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M] * num_hidden)
target_qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M] * num_hidden)
target_qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M] * num_hidden)
policy = TanhGaussianPolicy(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[M] * num_hidden)
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
policy,
)
replay_buffer = EnvReplayBuffer(
variant["replay_buffer_size"],
expl_env,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant["trainer_kwargs"])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"])
algorithm.train()
def experiment(variant):
import sys
from traffic.make_env import make_env
expl_env = make_env(args.exp_name)
eval_env = make_env(args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
qf = Mlp(
input_size=obs_dim,
output_size=action_dim,
**variant['qf_kwargs']
)
target_qf = copy.deepcopy(qf)
eval_policy = ArgmaxDiscretePolicy(qf)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space, variant['epsilon']),
eval_policy,
)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
expl_policy,
)
qf_criterion = nn.MSELoss()
trainer = DoubleDQNTrainer(
qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
**variant['trainer_kwargs']
)
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
log_path_function = get_traffic_path_information,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = NormalizedBoxEnv(HalfCheetahEnv())
eval_env = NormalizedBoxEnv(HalfCheetahEnv())
obs_dim = expl_env.observation_space.low.size
action_dim = expl_env.action_space.low.size
qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant["qf_kwargs"])
qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant["qf_kwargs"])
target_qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant["qf_kwargs"])
target_qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant["qf_kwargs"])
policy = TanhMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant["policy_kwargs"])
target_policy = TanhMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant["policy_kwargs"])
es = GaussianStrategy(
action_space=expl_env.action_space,
max_sigma=0.1,
min_sigma=0.1, # Constant sigma
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es, policy=policy)
eval_path_collector = MdpPathCollector(eval_env, policy)
expl_path_collector = MdpPathCollector(expl_env, exploration_policy)
replay_buffer = EnvReplayBuffer(variant["replay_buffer_size"], expl_env)
trainer = TD3Trainer(policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
target_policy=target_policy,
**variant["trainer_kwargs"])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant, data):
# make new env, reloading with data['evaluation/env'] seems to make bug
eval_env = gym.make("panda-v0", **{"headless": variant["headless"]})
eval_env.seed(variant['seed'])
expl_env = eval_env
qf1 = data['trainer/qf1']
qf2 = data['trainer/qf2']
target_qf1 = data['trainer/target_qf1']
target_qf2 = data['trainer/target_qf2']
policy = data['trainer/policy']
eval_policy = data["evaluation/policy"]
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = CustomMDPPathCollector(eval_env, )
buffer_filename = None
if variant['buffer_filename'] is not None:
buffer_filename = variant['buffer_filename']
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
if variant['load_buffer'] and buffer_filename is not None:
replay_buffer.load_buffer(buffer_filename)
else:
dataset = get_dataset(variant["h5path"], eval_env)
load_hdf5(d4rl.qlearning_dataset(eval_env, dataset), replay_buffer)
trainer = CQLTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
eval_both=True,
batch_rl=variant['load_buffer'],
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train(start_epoch=variant["start_epoch"])
def experiment(variant):
num_agent = variant['num_agent']
from cartpole import CartPoleEnv
from rlkit.envs.ma_wrappers import MAProbDiscreteEnv
expl_env = MAProbDiscreteEnv(CartPoleEnv(mode=4))
eval_env = MAProbDiscreteEnv(CartPoleEnv(mode=4))
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
qf_n, policy_n, target_qf_n, target_policy_n, exploration_policy_n = \
[], [], [], [], []
for i in range(num_agent):
qf = FlattenMlp(input_size=(obs_dim * num_agent +
action_dim * num_agent),
output_size=1,
**variant['qf_kwargs'])
policy = SoftmaxMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
exploration_policy = policy
qf_n.append(qf)
policy_n.append(policy)
target_qf_n.append(target_qf)
target_policy_n.append(target_policy)
exploration_policy_n.append(exploration_policy)
eval_path_collector = MAMdpPathCollector(eval_env, policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, exploration_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
trainer = MADDPGTrainer(qf_n=qf_n,
target_qf_n=target_qf_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def sac(variant):
expl_env = gym.make(variant["env_name"])
eval_env = gym.make(variant["env_name"])
expl_env.seed(variant["seed"])
eval_env.set_eval()
mode = variant["mode"]
archi = variant["archi"]
if mode == "her":
variant["her"] = dict(
observation_key="observation",
desired_goal_key="desired_goal",
achieved_goal_key="achieved_goal",
representation_goal_key="representation_goal",
)
replay_buffer = get_replay_buffer(variant, expl_env)
qf1, qf2, target_qf1, target_qf2, policy, shared_base = get_networks(
variant, expl_env)
expl_policy = policy
eval_policy = MakeDeterministic(policy)
expl_path_collector, eval_path_collector = get_path_collector(
variant, expl_env, eval_env, expl_policy, eval_policy)
mode = variant["mode"]
trainer = SACTrainer(
env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant["trainer_kwargs"],
)
if mode == "her":
trainer = HERTrainer(trainer)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"],
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
from cartpole import CartPoleEnv
from rlkit.envs.wrappers import ProbDiscreteEnv
expl_env = ProbDiscreteEnv(CartPoleEnv(mode=2))
eval_env = ProbDiscreteEnv(CartPoleEnv(mode=2))
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
# import gym
# from rlkit.envs.wrappers import ProbDiscreteEnv
# expl_env = ProbDiscreteEnv(gym.make('CartPole-v0'))
# eval_env = ProbDiscreteEnv(gym.make('CartPole-v0'))
# obs_dim = eval_env.observation_space.low.size
# action_dim = eval_env.action_space.low.size
qf = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'])
policy = SoftmaxMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
eval_path_collector = MdpPathCollector(eval_env, policy)
# remove this since need action to be a prob
# exploration_policy = PolicyWrappedWithExplorationStrategy(
# exploration_strategy=OUStrategy(action_space=expl_env.action_space),
# policy=policy,
# )
exploration_policy = policy
expl_path_collector = MdpPathCollector(expl_env, exploration_policy)
replay_buffer = EnvReplayBuffer(variant['replay_buffer_size'], expl_env)
trainer = DDPGTrainer(qf=qf,
target_qf=target_qf,
policy=policy,
target_policy=target_policy,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
eval_env = NormalizedBoxEnv(HalfCheetahEnv())
expl_env = NormalizedBoxEnv(HalfCheetahEnv())
# Or for a specific version:
# import gym
# env = NormalizedBoxEnv(gym.make('HalfCheetah-v1'))
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
qf = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
eval_path_collector = MdpPathCollector(eval_env, policy)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(action_space=expl_env.action_space),
policy=policy,
)
expl_path_collector = MdpPathCollector(expl_env, exploration_policy)
replay_buffer = EnvReplayBuffer(variant['replay_buffer_size'], expl_env)
trainer = DDPGTrainer(
qf=qf,
target_qf=target_qf,
policy=policy,
target_policy=target_policy,
**variant['trainer_kwargs']
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
from cartpole import CartPoleEnv
expl_env = NormalizedBoxEnv(CartPoleEnv(mode=0))
eval_env = NormalizedBoxEnv(CartPoleEnv(mode=0))
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
qf = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
eval_path_collector = MdpPathCollector(eval_env, policy)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(action_space=expl_env.action_space),
policy=policy,
)
expl_path_collector = MdpPathCollector(expl_env, exploration_policy)
replay_buffer = EnvReplayBuffer(variant['replay_buffer_size'], expl_env)
trainer = DDPGTrainer(
qf=qf,
target_qf=target_qf,
policy=policy,
target_policy=target_policy,
**variant['trainer_kwargs']
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
from cartpole import CartPoleEnv
expl_env = CartPoleEnv(mode=2)
eval_env = CartPoleEnv(mode=2)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
qf = Mlp(input_size=obs_dim,
output_size=action_dim,
**variant['qf_kwargs'])
target_qf = copy.deepcopy(qf)
eval_policy = ArgmaxDiscretePolicy(qf)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space, variant['epsilon']),
eval_policy,
)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
expl_policy,
)
replay_buffer = PrioritizedReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
qf_criterion = nn.MSELoss()
trainer = DQNTrainer(qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
replay_buffer=replay_buffer,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = NormalizedBoxEnv(HalfCheetahEnv())
eval_env = NormalizedBoxEnv(HalfCheetahEnv())
obs_dim = expl_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
M = variant["layer_size"]
qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M])
qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M])
target_qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M])
target_qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M])
policy = TanhGaussianPolicy(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[M, M])
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(eval_env, eval_policy)
expl_path_collector = MdpPathCollector(expl_env, policy)
replay_buffer = EnvReplayBuffer(variant["replay_buffer_size"], expl_env)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant["trainer_kwargs"])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env = gym.make('RLkitUR-v0')._start_ros_services()
eval_env = gym.make('RLkitUR-v0')
expl_env = gym.make('RLkitUR-v0')
eval_env = NormalizedBoxEnv(eval_env)
expl_env = NormalizedBoxEnv(expl_env)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
print("obs_dim: ", obs_dim)
print("action_dim: ", action_dim)
qf = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'])
policy = TanhMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
eval_path_collector = MdpPathCollector(eval_env, policy)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(action_space=expl_env.action_space),
policy=policy,
)
expl_path_collector = MdpPathCollector(expl_env, exploration_policy)
replay_buffer = EnvReplayBuffer(variant['replay_buffer_size'], expl_env)
trainer = DDPGTrainer(qf=qf,
target_qf=target_qf,
policy=policy,
target_policy=target_policy,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = make_env()
eval_env = make_env()
obs_dim = expl_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
M = variant['layer_size']
qf1 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
qf2 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
target_qf1 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
target_qf2 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
policy = TanhGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[M, M],
)
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
policy,
)
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
num_agent = variant['num_agent']
from differential_game import DifferentialGame
expl_env = DifferentialGame(game_name=args.exp_name)
eval_env = DifferentialGame(game_name=args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
qf1_n, qf2_n, cactor_n, policy_n, target_qf1_n, target_qf2_n, target_policy_n, expl_policy_n, eval_policy_n = \
[], [], [], [], [], [], [], [], []
for i in range(num_agent):
qf1 = FlattenMlp(input_size=(obs_dim * num_agent +
action_dim * num_agent),
output_size=1,
**variant['qf_kwargs'])
qf2 = FlattenMlp(input_size=(obs_dim * num_agent +
action_dim * num_agent),
output_size=1,
**variant['qf_kwargs'])
cactor = TanhMlpPolicy(input_size=(obs_dim * num_agent + action_dim *
(num_agent - 1)),
output_size=action_dim,
**variant['cactor_kwargs'])
policy = TanhMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
target_qf1 = copy.deepcopy(qf1)
target_qf2 = copy.deepcopy(qf2)
target_policy = copy.deepcopy(policy)
eval_policy = policy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(
action_space=expl_env.action_space),
policy=policy,
)
qf1_n.append(qf1)
qf2_n.append(qf2)
cactor_n.append(cactor)
policy_n.append(policy)
target_qf1_n.append(target_qf1)
target_qf2_n.append(target_qf2)
target_policy_n.append(target_policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
trainer = PRGTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
cactor_n=cactor_n,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env = gym.make('RLkitGoalUR-v0')._start_ros_services()
eval_env = gym.make('RLkitGoalUR-v0')
expl_env = gym.make('RLkitGoalUR-v0')
observation_key = 'observation'
desired_goal_key = 'desired_goal'
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
replay_buffer = ObsDictRelabelingBuffer(
env=eval_env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**variant['replay_buffer_kwargs'])
obs_dim = eval_env.observation_space.spaces['observation'].low.size
action_dim = eval_env.action_space.low.size
goal_dim = eval_env.observation_space.spaces['desired_goal'].low.size
qf1 = FlattenMlp(input_size=obs_dim + action_dim + goal_dim,
output_size=1,
**variant['qf_kwargs'])
qf2 = FlattenMlp(input_size=obs_dim + action_dim + goal_dim,
output_size=1,
**variant['qf_kwargs'])
target_qf1 = FlattenMlp(input_size=obs_dim + action_dim + goal_dim,
output_size=1,
**variant['qf_kwargs'])
target_qf2 = FlattenMlp(input_size=obs_dim + action_dim + goal_dim,
output_size=1,
**variant['qf_kwargs'])
policy = TanhGaussianPolicy(obs_dim=obs_dim + goal_dim,
action_dim=action_dim,
**variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['sac_trainer_kwargs'])
trainer = HERTrainer(trainer)
eval_path_collector = GoalConditionedPathCollector(
eval_env,
eval_policy,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = GoalConditionedPathCollector(
expl_env,
policy,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
num_agent = variant['num_agent']
from cartpole import CartPoleEnv
from rlkit.envs.ma_wrappers import MAProbDiscreteEnv
expl_env = CartPoleEnv(mode=4)
eval_env = CartPoleEnv(mode=4)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
qf_n, cactor_n, policy_n, target_qf_n, target_cactor_n, target_policy_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], [], [], []
for i in range(num_agent):
qf = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
**variant['qf_kwargs']
)
cactor = GumbelSoftmaxMlpPolicy(
input_size=(obs_dim*num_agent+action_dim*(num_agent-1)),
output_size=action_dim,
**variant['cactor_kwargs']
)
policy = GumbelSoftmaxMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
target_qf = copy.deepcopy(qf)
target_cactor = copy.deepcopy(cactor)
target_policy = copy.deepcopy(policy)
eval_policy = ArgmaxDiscretePolicy(policy,use_preactivation=True)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space),
eval_policy,
)
qf_n.append(qf)
cactor_n.append(cactor)
policy_n.append(policy)
target_qf_n.append(target_qf)
target_cactor_n.append(target_cactor)
target_policy_n.append(target_policy)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'], expl_env, num_agent=num_agent)
trainer = PRGTrainer(
env=expl_env,
qf_n=qf_n,
target_qf_n=target_qf_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
cactor_n=cactor_n,
target_cactor_n=target_cactor_n,
**variant['trainer_kwargs']
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import multiworld
multiworld.register_all_envs()
eval_env = gym.make('SawyerPushXYZEnv-v0')
expl_env = gym.make('SawyerPushXYZEnv-v0')
observation_key = 'state_observation'
desired_goal_key = 'state_desired_goal'
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
es = GaussianAndEpislonStrategy(
action_space=expl_env.action_space,
max_sigma=.2,
min_sigma=.2, # constant sigma
epsilon=.3,
)
obs_dim = expl_env.observation_space.spaces['observation'].low.size
goal_dim = expl_env.observation_space.spaces['desired_goal'].low.size
action_dim = expl_env.action_space.low.size
qf1 = FlattenMlp(
input_size=obs_dim + goal_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
qf2 = FlattenMlp(
input_size=obs_dim + goal_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
target_qf1 = FlattenMlp(
input_size=obs_dim + goal_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
target_qf2 = FlattenMlp(
input_size=obs_dim + goal_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
policy = TanhMlpPolicy(
input_size=obs_dim + goal_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
target_policy = TanhMlpPolicy(
input_size=obs_dim + goal_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
replay_buffer = ObsDictRelabelingBuffer(
env=eval_env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**variant['replay_buffer_kwargs']
)
trainer = TD3Trainer(
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
target_policy=target_policy,
**variant['trainer_kwargs']
)
trainer = HERTrainer(trainer)
eval_path_collector = GoalConditionedPathCollector(
eval_env,
policy,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = GoalConditionedPathCollector(
expl_env,
expl_policy,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()