c.save_freq = None
c.eval_freq = None
c.eval_env = RogueEnvExt(
RogueEnv(
config_dict=rogue_config(2),
mex_steps=500,
stair_reward=50.0,
image_setting=EXPAND,
))
c.set_optimizer(lambda params: Adam(params, lr=1.0e-4, eps=1.0e-8))
CNN_PARAM = [(8, 1), (4, 1), (3, 1)]
c.set_net_fn(
"actor-critic",
rainy.net.actor_critic.ac_conv(
kernel_and_strides=CNN_PARAM,
output_dim=256,
),
)
c.nworkers = 32
c.nsteps = 125
c.value_loss_weight = 0.5
c.gae_lambda = 0.95
c.ppo_minibatch_size = (c.nworkers * c.nsteps) // 4
return c
if __name__ == "__main__":
cli.run_cli(config,
rainy.agents.PPOAgent,
script_path=os.path.realpath(__file__))
from torch.optim import RMSprop
def config() -> Config:
c = Config()
env_use = "Pong"
c.set_env(lambda: Atari(env_use, frame_stack=False))
c.set_optimizer(
lambda params: RMSprop(params, lr=7e-4, alpha=0.99, eps=1e-5))
c.set_net_fn('actor-critic', net.actor_critic.ac_conv(rnn=net.GruBlock))
#c.set_net_fn('actor-critic', net.actor_critic.ac_conv())
c.nworkers = 16
c.nsteps = 5
c.set_parallel_env(atari_parallel())
c.grad_clip = 0.5
c.value_loss_weight = 0.5
c.use_gae = False
c.max_steps = int(2e7)
c.eval_env = Atari(env_use)
c.use_reward_monitor = True
c.eval_deterministic = False
c.episode_log_freq = 100
c.eval_freq = None
c.save_freq = None
print("GRU on Pong!")
return c
if __name__ == '__main__':
cli.run_cli(config(), A2cAgent, script_path=os.path.realpath(__file__))
import os
from rainy import Config
from rainy.agents import PpoAgent
import rainy.utils.cli as cli
from rogue_gym.envs import ImageSetting, StatusFlag, DungeonType
from torch.optim import Adam
from env import set_env
from net import a2c_conv
EXPAND = ImageSetting(dungeon=DungeonType.SYMBOL, status=StatusFlag.EMPTY)
AGENT = PpoAgent
def config() -> Config:
c = Config()
set_env(c, EXPAND)
c.set_optimizer(lambda params: Adam(params, lr=2.5e-4, eps=1.0e-4))
c.set_net_fn('actor-critic', a2c_conv())
c.grad_clip = 0.5
c.episode_log_freq = 100
c.eval_deterministic = False
return c
if __name__ == '__main__':
cli.run_cli(config(), AGENT, script_path=os.path.realpath(__file__))
import os
import rainy.utils.cli as cli
from rainy.envs import Atari, atari_parallel
from int_rew import rnd
def config(envname: str = "MontezumaRevenge") -> rnd.RNDConfig:
c = rnd.RNDConfig()
c.set_env(lambda: Atari(envname, cfg=rnd.atari_config(), frame_stack=False))
c.set_parallel_env(atari_parallel())
c.max_steps = int(1e8) * 6
c.grad_clip = 1.0
# ppo params
c.nworkers = 64
c.nsteps = 128
c.value_loss_weight = 0.5
c.gae_lambda = 0.95
c.ppo_minibatch_size = (c.nworkers * c.nsteps) // 4
c.auxloss_use_ratio = min(1.0, 32.0 / c.nworkers)
c.use_reward_monitor = True
# eval settings
c.eval_env = Atari(envname, cfg=rnd.atari_config())
c.episode_log_freq = 1000
c.eval_freq = None
c.save_freq = int(1e8)
return c
if __name__ == "__main__":
cli.run_cli(config, rnd.RNDAgent, script_path=os.path.realpath(__file__))
from os.path import realpath
import ppo_atari
import rainy
from rainy.envs import Atari, atari_parallel
import rainy.utils.cli as cli
def config() -> rainy.Config:
c = ppo_atari.config()
c.set_env(lambda: Atari('Breakout', flicker_frame=True, frame_stack=False))
c.set_parallel_env(atari_parallel(frame_stack=False))
c.set_net_fn('actor-critic',
rainy.net.actor_critic.ac_conv(rnn=rainy.net.GruBlock))
c.eval_env = Atari('Breakout', frame_stack=False)
return c
if __name__ == '__main__':
cli.run_cli(config(),
rainy.agents.PpoAgent,
script_path=realpath(__file__))
import os
import rainy
from rainy.utils.cli import run_cli
from rainy.envs import MultiProcEnv
from torch.optim import Adam
def config() -> rainy.Config:
c = rainy.Config()
c.max_steps = int(1e6)
c.nworkers = 8
c.nsteps = 32
c.set_parallel_env(MultiProcEnv)
c.set_optimizer(lambda params: Adam(params, lr=2.5e-4, eps=1.0e-4))
c.value_loss_weight = 0.1
c.grad_clip = 0.1
c.gae_lambda = 0.95
c.ppo_minibatch_size = 64
c.use_gae = True
c.ppo_clip = 0.2
c.eval_freq = None
# c.set_net_fn('actor-critic', rainy.net.actor_critic.fc_shared(rnn=rainy.net.GruBlock))
return c
if __name__ == '__main__':
run_cli(config(),
rainy.agents.PpoAgent,
script_path=os.path.realpath(__file__))
lambda: Atari(envname, cfg=rnd.atari_config(), frame_stack=False))
c.set_optimizer(lambda params: Adam(params, lr=rnd_lr),
key="rnd_separated")
c.set_parallel_env(atari_parallel())
c.max_steps = int(1e8) * 6
c.grad_clip = 1.0
# ppo params
c.nworkers = 64
c.nsteps = 128
c.value_loss_weight = 0.5
c.gae_lambda = 0.95
c.ppo_minibatch_size = (c.nworkers * c.nsteps) // 4
c.auxloss_use_ratio = min(1.0, 32.0 / c.nworkers)
c.use_reward_monitor = True
# eval settings
c.eval_env = Atari(envname, cfg=rnd.atari_config())
c.episode_log_freq = 100
c.eval_freq = None
c.save_freq = int(1e8)
return c
if __name__ == "__main__":
options = [click.Option(["--rnd-lr"], type=float, default=5.0e-5)]
cli.run_cli(
config,
rnd.TunedRNDAgent,
os.path.realpath(__file__),
options,
)
from rainy.agents import DoubleDqnAgent
from rainy.envs import Atari
from rainy.lib.explore import EpsGreedy, LinearCooler
import rainy.utils.cli as cli
from torch.optim import RMSprop
def config() -> Config:
c = Config()
c.set_env(lambda: Atari('Breakout'))
c.set_optimizer(lambda params: RMSprop(
params, lr=0.00025, alpha=0.95, eps=0.01, centered=True))
c.set_explorer(lambda: EpsGreedy(1.0, LinearCooler(1.0, 0.1, int(1e6))))
c.set_net_fn('value', net.value.dqn_conv())
c.replay_size = int(1e6)
c.batch_size = 32
c.train_start = 50000
c.sync_freq = 10000
c.max_steps = int(2e7)
c.eval_env = Atari('Breakout', episodic_life=False)
c.eval_freq = None
c.seed = 1
c.use_reward_monitor = True
return c
if __name__ == '__main__':
cli.run_cli(config(),
DoubleDqnAgent,
script_path=os.path.realpath(__file__))