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1eefeba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import argparse
import torch
from bpql import BPQLAgent
from trainer import Trainer
from utils import set_seed, make_delayed_env
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--env-name', default='HalfCheetah-v3', type=str)
parser.add_argument('--obs-delayed-steps', default=4, type=int) # Delayed timesteps (Observation, Reward)
parser.add_argument('--act-delayed-steps', default=5, type=int) # Delayed timesteps (Action)
parser.add_argument('--random-seed', default=-1, type=int)
parser.add_argument('--eval_flag', default=True, type=bool)
parser.add_argument('--eval-freq', default=5000, type=int)
parser.add_argument('--eval-episode', default=5, type=int)
parser.add_argument('--automating-temperature', default=True, type=bool)
parser.add_argument('--temperature', default=0.2, type=float)
parser.add_argument('--start-step', default=10000, type=int)
parser.add_argument('--max-step', default=1000000, type=int)
parser.add_argument('--update_after', default=1000, type=int)
parser.add_argument('--hidden-dims', default=(256, 256))
parser.add_argument('--batch-size', default=256, type=int)
parser.add_argument('--buffer-size', default=1000000, type=int)
parser.add_argument('--update-every', default=50, type=int)
parser.add_argument('--log_std_bound', default=[-20, 2])
parser.add_argument('--gamma', default=0.99, type=float)
parser.add_argument('--actor-lr', default=3e-4, type=float)
parser.add_argument('--critic-lr', default=3e-4, type=float)
parser.add_argument('--temperature-lr', default=3e-4, type=float)
parser.add_argument('--tau', default=0.005, type=float)
parser.add_argument('--show-loss', default=False, type=bool)
args = parser.parse_args()
# Set Device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Set Seed
random_seed = set_seed(args.random_seed)
# Create Delayed Environment
env, eval_env = make_delayed_env(args, random_seed, obs_delayed_steps=args.obs_delayed_steps, act_delayed_steps=args.act_delayed_steps)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
action_bound = [env.action_space.low[0], env.action_space.high[0]]
print(f"Environment: {args.env_name}, Obs. Delayed Steps: {args.obs_delayed_steps}, Act. Delayed Steps: {args.act_delayed_steps}, Random Seed: {args.random_seed}", "\n")
# Create Agent
agent = BPQLAgent(args, state_dim, action_dim, action_bound, env.action_space, device)
# Create Trainer & Train
trainer = Trainer(env, eval_env, agent, args)
trainer.train()
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