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examples.py
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examples.py
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#######################################################################
# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
# Permission given to modify the code as long as you keep this #
# declaration at the top #
#######################################################################
from deep_rl import *
# DQN
def dqn_feature(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda: VanillaNet(config.action_dim, FCBody(config.state_dim))
# config.network_fn = lambda: DuelingNet(config.action_dim, FCBody(config.state_dim))
# config.replay_fn = lambda: Replay(memory_size=int(1e4), batch_size=10)
config.replay_fn = lambda: AsyncReplay(memory_size=int(1e4), batch_size=10)
config.random_action_prob = LinearSchedule(1.0, 0.1, 1e4)
config.discount = 0.99
config.target_network_update_freq = 200
config.exploration_steps = 1000
# config.double_q = True
config.double_q = False
config.sgd_update_frequency = 4
config.gradient_clip = 5
config.eval_interval = int(5e3)
config.max_steps = 1e5
config.async_actor = False
run_steps(DQNAgent(config))
def dqn_pixel(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.optimizer_fn = lambda params: torch.optim.RMSprop(
params, lr=0.00025, alpha=0.95, eps=0.01, centered=True)
config.network_fn = lambda: VanillaNet(config.action_dim, NatureConvBody(in_channels=config.history_length))
# config.network_fn = lambda: DuelingNet(config.action_dim, NatureConvBody(in_channels=config.history_length))
config.random_action_prob = LinearSchedule(1.0, 0.01, 1e6)
# config.replay_fn = lambda: Replay(memory_size=int(1e6), batch_size=32)
config.replay_fn = lambda: AsyncReplay(memory_size=int(1e6), batch_size=32)
config.batch_size = 32
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.target_network_update_freq = 10000
config.exploration_steps = 50000
config.sgd_update_frequency = 4
config.gradient_clip = 5
config.history_length = 4
# config.double_q = True
config.double_q = False
config.max_steps = int(2e7)
run_steps(DQNAgent(config))
# QR DQN
def quantile_regression_dqn_feature(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda: QuantileNet(config.action_dim, config.num_quantiles, FCBody(config.state_dim))
# config.replay_fn = lambda: Replay(memory_size=int(1e4), batch_size=10)
config.replay_fn = lambda: AsyncReplay(memory_size=int(1e4), batch_size=10)
config.random_action_prob = LinearSchedule(1.0, 0.1, 1e4)
config.discount = 0.99
config.target_network_update_freq = 200
config.exploration_steps = 100
config.num_quantiles = 20
config.gradient_clip = 5
config.sgd_update_frequency = 4
config.eval_interval = int(5e3)
config.max_steps = 1e5
run_steps(QuantileRegressionDQNAgent(config))
def quantile_regression_dqn_pixel(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.optimizer_fn = lambda params: torch.optim.Adam(params, lr=0.00005, eps=0.01 / 32)
config.network_fn = lambda: QuantileNet(config.action_dim, config.num_quantiles, NatureConvBody())
config.random_action_prob = LinearSchedule(1.0, 0.01, 1e6)
# config.replay_fn = lambda: Replay(memory_size=int(1e6), batch_size=32)
config.replay_fn = lambda: AsyncReplay(memory_size=int(1e6), batch_size=32)
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.target_network_update_freq = 10000
config.exploration_steps = 50000
config.sgd_update_frequency = 4
config.gradient_clip = 5
config.num_quantiles = 200
config.max_steps = int(2e7)
run_steps(QuantileRegressionDQNAgent(config))
# C51
def categorical_dqn_feature(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda: CategoricalNet(config.action_dim, config.categorical_n_atoms, FCBody(config.state_dim))
config.random_action_prob = LinearSchedule(1.0, 0.1, 1e4)
# config.replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
config.replay_fn = lambda: AsyncReplay(memory_size=10000, batch_size=10)
config.discount = 0.99
config.target_network_update_freq = 200
config.exploration_steps = 100
config.categorical_v_max = 100
config.categorical_v_min = -100
config.categorical_n_atoms = 50
config.gradient_clip = 5
config.sgd_update_frequency = 4
config.eval_interval = int(5e3)
config.max_steps = 1e5
run_steps(CategoricalDQNAgent(config))
def categorical_dqn_pixel(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.optimizer_fn = lambda params: torch.optim.Adam(params, lr=0.00025, eps=0.01 / 32)
config.network_fn = lambda: CategoricalNet(config.action_dim, config.categorical_n_atoms, NatureConvBody())
config.random_action_prob = LinearSchedule(1.0, 0.01, 1e6)
# config.replay_fn = lambda: Replay(memory_size=int(1e6), batch_size=32)
config.replay_fn = lambda: AsyncReplay(memory_size=int(1e6), batch_size=32)
config.discount = 0.99
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.target_network_update_freq = 10000
config.exploration_steps = 50000
config.categorical_v_max = 10
config.categorical_v_min = -10
config.categorical_n_atoms = 51
config.sgd_update_frequency = 4
config.gradient_clip = 0.5
config.max_steps = int(2e7)
run_steps(CategoricalDQNAgent(config))
# A2C
def a2c_feature(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.num_workers = 5
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda: CategoricalActorCriticNet(
config.state_dim, config.action_dim, FCBody(config.state_dim, gate=F.tanh))
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.95
config.entropy_weight = 0.01
config.rollout_length = 5
config.gradient_clip = 0.5
run_steps(A2CAgent(config))
def a2c_pixel(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.num_workers = 16
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=1e-4, alpha=0.99, eps=1e-5)
config.network_fn = lambda: CategoricalActorCriticNet(config.state_dim, config.action_dim, NatureConvBody())
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 1.0
config.entropy_weight = 0.01
config.rollout_length = 5
config.gradient_clip = 5
config.max_steps = int(2e7)
run_steps(A2CAgent(config))
def a2c_continuous(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.num_workers = 16
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=0.0007)
config.network_fn = lambda: GaussianActorCriticNet(
config.state_dim, config.action_dim,
actor_body=FCBody(config.state_dim), critic_body=FCBody(config.state_dim))
config.discount = 0.99
config.use_gae = True
config.gae_tau = 1.0
config.entropy_weight = 0.01
config.rollout_length = 5
config.gradient_clip = 5
config.max_steps = int(2e7)
run_steps(A2CAgent(config))
# N-Step DQN
def n_step_dqn_feature(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.num_workers = 5
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda: VanillaNet(config.action_dim, FCBody(config.state_dim))
config.random_action_prob = LinearSchedule(1.0, 0.1, 1e4)
config.discount = 0.99
config.target_network_update_freq = 200
config.rollout_length = 5
config.gradient_clip = 5
run_steps(NStepDQNAgent(config))
def n_step_dqn_pixel(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.num_workers = 16
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=1e-4, alpha=0.99, eps=1e-5)
config.network_fn = lambda: VanillaNet(config.action_dim, NatureConvBody())
config.random_action_prob = LinearSchedule(1.0, 0.05, 1e6)
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.target_network_update_freq = 10000
config.rollout_length = 5
config.gradient_clip = 5
config.max_steps = int(2e7)
run_steps(NStepDQNAgent(config))
# Option-Critic
def option_critic_feature(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.num_workers = 5
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda: OptionCriticNet(FCBody(config.state_dim), config.action_dim, num_options=2)
config.random_option_prob = LinearSchedule(1.0, 0.1, 1e4)
config.discount = 0.99
config.target_network_update_freq = 200
config.rollout_length = 5
config.termination_regularizer = 0.01
config.entropy_weight = 0.01
config.gradient_clip = 5
run_steps(OptionCriticAgent(config))
def option_critic_pixel(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.num_workers = 16
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=1e-4, alpha=0.99, eps=1e-5)
config.network_fn = lambda: OptionCriticNet(NatureConvBody(), config.action_dim, num_options=4)
config.random_option_prob = LinearSchedule(0.1)
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.target_network_update_freq = 10000
config.rollout_length = 5
config.gradient_clip = 5
config.max_steps = int(2e7)
config.entropy_weight = 0.01
config.termination_regularizer = 0.01
run_steps(OptionCriticAgent(config))
# PPO
def ppo_feature(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.num_workers = 5
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda: CategoricalActorCriticNet(config.state_dim, config.action_dim, FCBody(config.state_dim))
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.95
config.entropy_weight = 0.01
config.gradient_clip = 5
config.rollout_length = 128
config.optimization_epochs = 10
config.mini_batch_size = 32 * 5
config.ppo_ratio_clip = 0.2
config.log_interval = 128 * 5 * 10
run_steps(PPOAgent(config))
def ppo_pixel(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game, num_envs=config.num_workers)
config.eval_env = Task(config.game)
config.num_workers = 8
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=0.00025, alpha=0.99, eps=1e-5)
config.network_fn = lambda: CategoricalActorCriticNet(config.state_dim, config.action_dim, NatureConvBody())
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.95
config.entropy_weight = 0.01
config.gradient_clip = 0.5
config.rollout_length = 128
config.optimization_epochs = 3
config.mini_batch_size = 32 * 8
config.ppo_ratio_clip = 0.1
config.log_interval = 128 * 8
config.max_steps = int(2e7)
run_steps(PPOAgent(config))
def ppo_continuous(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.network_fn = lambda: GaussianActorCriticNet(
config.state_dim, config.action_dim, actor_body=FCBody(config.state_dim, gate=torch.tanh),
critic_body=FCBody(config.state_dim, gate=torch.tanh))
config.optimizer_fn = lambda params: torch.optim.Adam(params, 3e-4, eps=1e-5)
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.95
config.gradient_clip = 0.5
config.rollout_length = 2048
config.optimization_epochs = 10
config.mini_batch_size = 64
config.ppo_ratio_clip = 0.2
config.log_interval = 2048
config.max_steps = 1e6
config.state_normalizer = MeanStdNormalizer()
run_steps(PPOAgent(config))
# DDPG
def ddpg_continuous(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.max_steps = int(1e6)
config.eval_interval = int(1e4)
config.eval_episodes = 20
config.network_fn = lambda: DeterministicActorCriticNet(
config.state_dim, config.action_dim,
actor_body=FCBody(config.state_dim, (400, 300), gate=F.relu),
critic_body=TwoLayerFCBodyWithAction(
config.state_dim, config.action_dim, (400, 300), gate=F.relu),
actor_opt_fn=lambda params: torch.optim.Adam(params, lr=1e-4),
critic_opt_fn=lambda params: torch.optim.Adam(params, lr=1e-3))
config.replay_fn = lambda: Replay(memory_size=int(1e6), batch_size=64)
config.discount = 0.99
config.random_process_fn = lambda: OrnsteinUhlenbeckProcess(
size=(config.action_dim,), std=LinearSchedule(0.2))
config.warm_up = int(1e4)
config.target_network_mix = 1e-3
run_steps(DDPGAgent(config))
# TD3
def td3_continuous(**kwargs):
generate_tag(kwargs)
kwargs.setdefault('log_level', 0)
config = Config()
config.merge(kwargs)
config.task_fn = lambda: Task(config.game)
config.eval_env = config.task_fn()
config.max_steps = int(1e6)
config.eval_interval = int(1e4)
config.eval_episodes = 20
config.network_fn = lambda: TD3Net(
config.action_dim,
actor_body_fn=lambda: FCBody(config.state_dim, (400, 300), gate=F.relu),
critic_body_fn=lambda: FCBody(
config.state_dim+config.action_dim, (400, 300), gate=F.relu),
actor_opt_fn=lambda params: torch.optim.Adam(params, lr=1e-3),
critic_opt_fn=lambda params: torch.optim.Adam(params, lr=1e-3))
config.replay_fn = lambda: Replay(memory_size=int(1e6), batch_size=100)
config.discount = 0.99
config.random_process_fn = lambda: GaussianProcess(
size=(config.action_dim,), std=LinearSchedule(0.1))
config.td3_noise = 0.2
config.td3_noise_clip = 0.5
config.td3_delay = 2
config.warm_up = int(1e4)
config.target_network_mix = 5e-3
run_steps(TD3Agent(config))
if __name__ == '__main__':
mkdir('log')
mkdir('tf_log')
set_one_thread()
random_seed()
select_device(-1)
# select_device(0)
game = 'CartPole-v0'
# dqn_feature(game=game)
# quantile_regression_dqn_feature(game=game)
# categorical_dqn_feature(game=game)
# a2c_feature(game=game)
# n_step_dqn_feature(game=game)
# option_critic_feature(game=game)
# ppo_feature(game=game)
# game = 'HalfCheetah-v2'
game = 'Hopper-v2'
# a2c_continuous(game=game)
# ppo_continuous(game=game)
# ddpg_continuous(game=game)
td3_continuous(game=game)
game = 'BreakoutNoFrameskip-v4'
# dqn_pixel(game=game)
# quantile_regression_dqn_pixel(game=game)
# categorical_dqn_pixel(game=game)
# a2c_pixel(game=game)
# n_step_dqn_pixel(game=game)
# option_critic_pixel(game=game)
# ppo_pixel(game=game)