def training(**kwargs):
# Set logging level
if kwargs['debug']:
LOGGER.setLevel(logging.DEBUG)
else:
LOGGER.setLevel(logging.INFO)
agent = DQNAgent(environment=env,
action_space=[0, 1, 2, 3, 4, 5, 6, 7],
NN_arch=kwargs['NN_arch'],
maxIters=kwargs['max_iters'],
eta=0.00001,
epsilon=0.4,
discount=0.95,
weights_dir=kwargs['weights_dir'],
mem_size=10**5)
while True:
agent.learn(replay=kwargs['replay'],
frame_skipping=kwargs['frame_skipping'],
batch_size=kwargs['batch_size'])
if agent.numIters > agent.maxIters:
break
agent.save(agent.save_path % kwargs['max_iters'])
# return the agent object
return agent
scores, eps_history, steps_array = [], [], []
for i in range(n_games):
done = False
observation = env.reset()
score = 0
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
if not load_checkpoint:
agent.store_transition(observation, action, reward,
observation_, done)
agent.learn()
observation = observation_
n_steps += 1
scores.append(score)
steps_array.append(n_steps)
avg_score = np.mean(scores[-100:])
print('episode: ', i, 'score: ', score,
' average score %.1f' % avg_score,
'best score %.2f' % best_score, 'epsilon %.2f' % agent.epsilon,
'steps', n_steps)
if avg_score > best_score:
if not load_checkpoint:
agent.save_models()
best_score = avg_score
def dqn_train():
env = make_env('PongNoFrameskip-v4')
load_checkpoint = False
save_checkpoint = True
learning_enabled = True
rendering_enabled = False
n_games = 100
agent = DQNAgent(gamma=0.99,
epsilon=1.0,
lr=0.0001,
input_dims=(env.observation_space.shape),
n_actions=env.action_space.n,
mem_size=50000,
eps_min=0.1,
batch_size=32,
replace=1000,
eps_dec=1e-5,
chkpt_dir='models/',
algo='DQNAgent',
env_name='PongNoFrameskip-v4')
if load_checkpoint:
agent.load_models()
with open('models/best_score.pkl', 'rb') as file:
best_score = pickle.load(file)
else:
best_score = -np.inf
fname = agent.algo + '_' + agent.env_name + '_lr' + str(
agent.lr) + '_' + str(n_games) + 'games'
figure_file = 'plots/' + fname + '.png'
n_steps = 0
scores, eps_history, steps_array = [], [], []
for i in range(n_games):
done = False
observation = env.reset()
score = 0
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
if learning_enabled:
agent.store_transition(observation, action, reward,
observation_, int(done))
agent.learn()
if rendering_enabled:
env.render()
observation = observation_
n_steps += 1
scores.append(score)
steps_array.append(n_steps)
avg_score = np.mean(scores[-100:])
print('episode: ', i, 'score: ', score,
' average score %.1f' % avg_score,
'best score %.2f' % best_score, 'epsilon %.2f' % agent.epsilon,
'steps', n_steps)
if avg_score > best_score:
best_score = avg_score
if save_checkpoint:
agent.save_models()
with open('models/best_score.pkl', 'wb') as file:
pickle.dump(best_score, file)
eps_history.append(agent.epsilon)
if load_checkpoint and n_steps >= 18000:
break
x = [i + 1 for i in range(len(scores))]
plot_learning_curve(steps_array, scores, eps_history, figure_file)
