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import os
import torch
import matplotlib
import matplotlib.pyplot as plt
from pathlib import Path
from tqdm import trange
from agent import DQNAgent, DDQNAgent, MetricLogger
from wrappers import make_env
# set up matplotlib
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
from IPython import display
plt.ion()
env = make_env()
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/airstriker-dqn-new"
save_dir = Path(path)
isExist = os.path.exists(path)
if not isExist:
os.makedirs(path)
# Vanilla DQN
print("Training Vanilla DQN Agent!")
agent = DQNAgent(
state_dim=(1, 84, 84),
action_dim=env.action_space.n,
save_dir=save_dir,
batch_size=128,
checkpoint=checkpoint,
exploration_rate_decay=0.995,
exploration_rate_min=0.05,
training_frequency=1,
target_network_sync_frequency=500,
max_memory_size=50000,
learning_rate=0.0005,
)
# Double DQN
# print("Training DDQN Agent!")
# agent = DDQNAgent(
# state_dim=(1, 84, 84),
# action_dim=env.action_space.n,
# save_dir=save_dir,
# checkpoint=checkpoint,
# reset_exploration_rate=True,
# max_memory_size=max_memory_size
# )
logger = MetricLogger(save_dir)
def fill_memory(agent: DQNAgent, num_episodes=1000):
print("Filling up memory....")
for _ in trange(num_episodes):
state = env.reset()
done = False
while not done:
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
agent.cache(state, next_state, action, reward, done)
state = next_state
def train(agent: DQNAgent):
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
if done or info["gameover"] == 1:
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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