ml-reinforcement-learning / src /lunar-lander /run-lunar-dueling-dqn.py
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import os
import torch
from pathlib import Path
from agent import DuelingDQNAgent, DuelingDQNAgentWithStepDecay, MetricLogger
from wrappers import make_lunar
import os
from train import train, fill_memory
from params import hyperparams
env = make_lunar()
use_cuda = torch.cuda.is_available()
print(f"Using CUDA: {use_cuda}\n")
checkpoint = None
# checkpoint = Path('checkpoints/latest/airstriker_net_3.chkpt')
path = "checkpoints/lunar-lander-dueling-dqn-rc"
save_dir = Path(path)
isExist = os.path.exists(path)
if not isExist:
os.makedirs(path)
logger = MetricLogger(save_dir)
print("Training Dueling DQN Agent with step decay!")
agent = DuelingDQNAgentWithStepDecay(
state_dim=8,
action_dim=env.action_space.n,
save_dir=save_dir,
checkpoint=checkpoint,
**hyperparams
)
# print("Training Dueling DQN Agent!")
# agent = DuelingDQNAgent(
# state_dim=8,
# action_dim=env.action_space.n,
# save_dir=save_dir,
# checkpoint=checkpoint,
# **hyperparams
# )
# fill_memory(agent, env, 5000)
train(agent, env, logger)