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import os
import random, datetime
from pathlib import Path
import retro as gym
from collections import namedtuple, deque
from itertools import count
import torch
import matplotlib
import matplotlib.pyplot as plt
# from agent import MyAgent, MyDQN, MetricLogger
from cartpole import MyAgent, MetricLogger
from wrappers import make_env
import pickle
import gym
from tqdm import trange
# set up matplotlib
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
from IPython import display
plt.ion()
# env = make_env()
env = gym.make('CartPole-v1')
use_cuda = torch.cuda.is_available()
print(f"Using CUDA: {use_cuda}")
print()
path = "checkpoints/cartpole/latest"
save_dir = Path(path)
isExist = os.path.exists(path)
if not isExist:
os.makedirs(path)
# save_dir.mkdir(parents=True)
checkpoint = None
# checkpoint = Path('checkpoints/latest/airstriker_net_3.chkpt')
# For cartpole
n_actions = env.action_space.n
state = env.reset()
n_observations = len(state)
max_memory_size=100000
agent = MyAgent(
state_dim=n_observations,
action_dim=n_actions,
save_dir=save_dir,
checkpoint=checkpoint,
reset_exploration_rate=True,
max_memory_size=max_memory_size
)
# For airstriker
# agent = MyAgent(state_dim=(1, 84, 84), action_dim=env.action_space.n, save_dir=save_dir, checkpoint=checkpoint, reset_exploration_rate=True)
logger = MetricLogger(save_dir)
def fill_memory(agent: MyAgent):
print("Filling up memory....")
for _ in trange(max_memory_size):
state = env.reset()
done = False
while not done:
action = agent.act(state)
next_state, reward, done, info = env.step(action)
agent.cache(state, next_state, action, reward, done)
state = next_state
def train(agent: MyAgent):
episodes = 10000000
for e in range(episodes):
state = env.reset()
# Play the game!
while True:
# print(state.shape)
# Run agent on the state
action = agent.act(state)
# Agent performs action
next_state, reward, done, info = env.step(action)
# Remember
agent.cache(state, next_state, action, reward, done)
# Learn
q, loss = agent.learn()
# Logging
logger.log_step(reward, loss, q)
# Update state
state = next_state
# # Check if end of game (for airstriker)
# if done or info["gameover"] == 1:
# break
# Check if end of game (for cartpole)
if done:
break
logger.log_episode(e)
if e % 20 == 0:
logger.record(episode=e, epsilon=agent.exploration_rate, step=agent.curr_step)
fill_memory(agent)
train(agent)
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