import os
import gym
from rl.agents.trpo import TRPO
from rl.loops import EpisodeTrainLoop
from rl.metrics import AverageReturn, AverageEpisodeLength
from zoo.cartpole.core import PolicyNetwork, ValueFunctionNetwork
from zoo.utils import parse_args, get_output_dirs, evaluate_policy
if __name__ == '__main__':
args = parse_args()
ckpt_dir, log_dir = get_output_dirs(os.path.dirname(__file__), 'trpo',
args)
env = gym.make('CartPole-v0')
policy_fn = lambda: PolicyNetwork(env.observation_space.shape, env.
action_space.n)
vf_fn = lambda: ValueFunctionNetwork(env.observation_space.shape)
agent = TRPO(
env=env,
policy_fn=policy_fn,
vf_fn=vf_fn,
lr_vf=1e-3,
gamma=0.98,
lambda_=0.96,
delta=0.001,
replay_buffer_size=250 * 8,
policy_update_batch_size=512,
vf_update_batch_size=512,
vf_update_iterations=20,
import os
import gym
from rl.agents.ppo_penalty import PPOPenalty
from rl.loops import EpisodeTrainLoop
from rl.metrics import AverageEpisodeLength, AverageReturn
from zoo.cartpole.core import PolicyNetwork, ValueFunctionNetwork
from zoo.utils import parse_args, get_output_dirs, evaluate_policy
if __name__ == '__main__':
args = parse_args()
ckpt_dir, log_dir = get_output_dirs(os.path.dirname(__file__),
'ppo_penalty', args)
env = gym.make('CartPole-v0')
policy_fn = lambda: PolicyNetwork(env.observation_space.shape, env.
action_space.n)
vf_fn = lambda: ValueFunctionNetwork(env.observation_space.shape)
agent = PPOPenalty(
env=env,
policy_fn=policy_fn,
vf_fn=vf_fn,
lr_vf=1e-3,
lr_policy=1e-3,
gamma=0.98,
lambda_=0.96,
beta=1.0,
kl_target=0.001,
kl_tolerance=1.5,
beta_update_factor=2,
import os
from rl.agents.alpha_zero import AlphaZero
from zoo.connect_4.core import Connect4, PolicyAndValueFunctionNetwork
from zoo.utils import parse_args, get_output_dirs
if __name__ == '__main__':
args = parse_args()
ckpt_dir, log_dir, replay_dir = get_output_dirs(os.path.dirname(__file__),
'alpha_zero', args,
['ckpt', 'log', 'replay'])
game_fn = lambda: Connect4()
policy_and_vf_fn = lambda: PolicyAndValueFunctionNetwork(
observation_shape=Connect4.observation_space.shape,
n_actions=Connect4.action_space.n,
l2=3e-4,
)
agent = AlphaZero(
game_fn=game_fn,
policy_and_vf_fn=policy_and_vf_fn,
lr=1e-3,
mcts_n_steps=500,
mcts_tau=6 * 7,
mcts_eta=0.03,
mcts_epsilon=0.25,
mcts_c_puct=1,
n_self_play_workers=8,
update_iterations=10_000_000,
update_batch_size=64,
replay_buffer_size=50_000,
import os
import gym
from rl.agents.vpg_gae import VPGGAE
from rl.loops import EpisodeTrainLoop
from rl.metrics import AverageReturn, AverageEpisodeLength
from zoo.cartpole.core import PolicyNetwork, ValueFunctionNetwork
from zoo.utils import parse_args, get_output_dirs, evaluate_policy
if __name__ == '__main__':
args = parse_args()
ckpt_dir, log_dir = get_output_dirs(os.path.dirname(__file__), 'vpg_gae',
args)
env = gym.make('CartPole-v0')
policy_fn = lambda: PolicyNetwork(env.observation_space.shape, env.
action_space.n)
vf_fn = lambda: ValueFunctionNetwork(env.observation_space.shape)
agent = VPGGAE(
env=env,
policy_fn=policy_fn,
vf_fn=vf_fn,
lr_policy=1e-3,
lr_vf=1e-3,
gamma=0.98,
lambda_=0.96,
vf_update_iterations=20,
replay_buffer_size=250 * 2,
policy_update_batch_size=256,
vf_update_batch_size=256,
import os
from rl.agents.alpha_zero import AlphaZero
from zoo.tic_tac_toe.core import TicTacToe, PolicyAndValueFunctionNetwork
from zoo.utils import parse_args, get_output_dirs
if __name__ == '__main__':
args = parse_args()
ckpt_dir, log_dir = get_output_dirs(os.path.dirname(__file__), 'alpha_zero', args)
game_fn = lambda: TicTacToe()
policy_and_vf_fn = lambda: PolicyAndValueFunctionNetwork(
observation_shape=TicTacToe.observation_space.shape,
n_actions=TicTacToe.action_space.n,
l2=1e-3,
)
agent = AlphaZero(
game_fn=game_fn,
policy_and_vf_fn=policy_and_vf_fn,
lr=1e-3,
mcts_n_steps=100,
mcts_tau=9,
mcts_eta=0.03,
mcts_epsilon=0.25,
mcts_c_puct=1,
n_self_play_workers=8,
update_iterations=10_000,
update_batch_size=64,
replay_buffer_size=10_000,
ckpt_dir=ckpt_dir,
log_dir=log_dir,