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Major update

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This commit is contained in:
Shangtong Zhang
2017-11-11 20:52:57 -07:00
parent 8c197e183c
commit c1fdb1bd2f
17 changed files with 48 additions and 199 deletions
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main.py
+12 -48
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@@ -201,9 +201,10 @@ def a3c_continuous():
def p3o_continuous():
config = Config()
# config.task_fn = lambda: Pendulum()
config.task_fn = lambda: Pendulum()
config.task_fn = lambda: BipedalWalker()
# config.task_fn = lambda: BipedalWalkerHardcore()
config.task_fn = lambda: Roboschool('RoboschoolInvertedPendulum-v1')
# config.task_fn = lambda: Roboschool('RoboschoolInvertedPendulum-v1')
# config.task_fn = lambda: Roboschool('RoboschoolAnt-v1')
task = config.task_fn()
config.actor_network_fn = lambda: GaussianActorNet(task.state_dim, task.action_dim,
@@ -214,60 +215,29 @@ def p3o_continuous():
config.critic_optimizer_fn = lambda params: torch.optim.Adam(params, 0.001)
config.policy_fn = lambda: GaussianPolicy()
# config.replay_fn = lambda: GeneralReplay(memory_size=2048, batch_size=2048)
config.replay_fn = lambda: GeneralReplay(memory_size=2048, batch_size=64)
config.replay_fn = lambda: GeneralReplay(memory_size=2048, batch_size=2048)
config.worker = ProximalPolicyOptimization
config.discount = 0.99
config.gae_tau = 0.97
config.num_workers = 8
config.num_workers = 6
config.test_interval = 1
config.test_repetitions = 1
config.max_episode_length = task.max_episode_steps
config.entropy_weight = 0
config.gradient_clip = 20
config.rollout_length = 10000
config.optimize_epochs = 10
config.optimize_epochs = 1
config.ppo_ratio_clip = 0.2
config.logger = Logger('./log', gym.logger)
agent = AsyncAgent(config)
agent.run()
def ddpg_continuous():
config = Config()
# config.task_fn = lambda: Pendulum()
# config.task_fn = lambda: BipedalWalker()
config.task_fn = lambda: ContinuousLunarLander()
# config.task_fn = lambda: Roboschool('RoboschoolInvertedPendulum-v1')
# config.task_fn = lambda: Roboschool('RoboschoolReacher-v1')
task = config.task_fn()
config.actor_network_fn = lambda: DeterministicActorNet(
task.state_dim, task.action_dim, F.tanh, 2, non_linear=F.relu, batch_norm=False)
config.critic_network_fn = lambda: DeterministicCriticNet(
task.state_dim, task.action_dim, non_linear=F.relu, batch_norm=False)
config.network_fn = lambda: DisjointActorCriticNet(config.actor_network_fn, config.critic_network_fn)
config.actor_optimizer_fn = lambda params: torch.optim.Adam(params, lr=1e-4)
config.critic_optimizer_fn =\
lambda params: torch.optim.Adam(params, lr=1e-3, weight_decay=0.01)
config.replay_fn = lambda: HighDimActionReplay(memory_size=1000000, batch_size=64)
config.discount = 0.99
config.max_episode_length = task.max_episode_steps
config.target_network_mix = 0.001
config.exploration_steps = 100
config.random_process_fn = \
lambda: OrnsteinUhlenbeckProcess(size=task.action_dim, theta=0.15, sigma=0.2,
n_steps_annealing=10000)
config.test_interval = 0
config.test_repetitions = 10
config.save_interval = 50
config.logger = Logger('./log', gym.logger)
run_episodes(DDPGAgent(config))
def addpg_continuous():
def d3pg_continuous():
config = Config()
# config.task_fn = lambda: Pendulum()
# config.task_fn = lambda: ContinuousLunarLander()
config.task_fn = lambda: Roboschool('RoboschoolInvertedPendulum-v1')
# config.task_fn = lambda: Roboschool('RoboschoolReacher-v1')
# config.task_fn = lambda: Roboschool('RoboschoolInvertedPendulum-v1')
config.task_fn = lambda: Roboschool('RoboschoolReacher-v1')
# config.task_fn = lambda: BipedalWalker()
task = config.task_fn()
config.actor_network_fn = lambda: DeterministicActorNet(
@@ -278,10 +248,8 @@ def addpg_continuous():
config.actor_optimizer_fn = lambda params: torch.optim.Adam(params, lr=1e-4)
config.critic_optimizer_fn =\
lambda params: torch.optim.Adam(params, lr=1e-4)
# config.replay_fn = lambda: HighDimActionReplay(memory_size=1000000, batch_size=64)
config.replay_fn = lambda: SharedReplay(memory_size=1000000, batch_size=64,
state_shape=(task.state_dim, ), action_shape=(task.action_dim, ))
# config.replay_fn = lambda: GeneralReplay(memory_size=256, batch_size=64)
config.discount = 0.99
config.max_episode_length = task.max_episode_steps
config.random_process_fn = \
@@ -291,27 +259,23 @@ def addpg_continuous():
config.num_workers = 6
config.min_memory_size = 50
config.target_network_mix = 0.001
# config.update_interval = 10
config.test_interval = 500
config.test_repetitions = 1
config.gradient_clip = 20
config.rollout_length = 16
config.optimize_epochs = 1
config.logger = Logger('./log', gym.logger)
agent = AsyncAgent(config)
agent.run()
if __name__ == '__main__':
gym.logger.setLevel(logging.DEBUG)
# gym.logger.setLevel(logging.INFO)
# gym.logger.setLevel(logging.DEBUG)
gym.logger.setLevel(logging.INFO)
# dqn_cart_pole()
# async_cart_pole()
# a3c_cart_pole()
# a3c_continuous()
# p3o_continuous()
# ddpg_continuous()
addpg_continuous()
d3pg_continuous()
# dqn_fruit()
# hrdqn_fruit()