def train_dqn(episodes, env, render_frequency=0):
now = datetime.datetime.now()
id = f'{now.hour}{now.minute}'
episode_rewards = []
agent = DQN(env, params)
best_score = 0
for episode in range(episodes):
rendering = render_frequency and episode % render_frequency == 0 and isinstance(
env, HeadlessSnake)
state = env.reset(
) # Reset enviroment before each episode to start fresh
if rendering:
renderer = Renderer(env, episode + 1)
env.update_episode(episode + 1)
# state = np.reshape(state, (1, env.state_space))
total_reward = 0
max_steps = 10000
for step in range(max_steps):
# 1. Find next action using the Epsilon-Greedy exploration Strategy
action = agent.get_action(state)
# 2. perform action in enviroment
next_state, reward, done, _ = env.step(action)
total_reward += reward
# next_state = np.reshape(next_state, (1, env.state_space))
if rendering:
renderer.update()
# 3. Update the Q-function (train model)
agent.remember(state, action, reward, next_state, done)
agent.train_with_experience_replay()
# 4. Change exploration vs. explotation probability
agent.update_exploration_strategy(episode)
state = next_state
if done:
print(
f'episode: {episode+1}/{episodes}, score: {total_reward}, steps: {step}, '
f'epsilon: {agent.epsilon}, highscore: {env.maximum}')
save_model(id, agent, best_score, total_reward)
break
if rendering:
renderer.bye()
save_model(id, agent, best_score, total_reward)
episode_rewards.append(total_reward)
return episode_rewards
def test_dqn(env):
agent = DQN(env, params)
agent.load_model(sys.argv[1], sys.argv[2])
state = env.reset() # Reset enviroment before each episode to start fresh
state = np.reshape(state, (1, env.state_space))
max_steps = 10000
total_reward = 0
for step in range(max_steps):
action = agent.get_action(state)
next_state, reward, done, _ = env.step(action)
state = np.reshape(next_state, (1, env.state_space))
total_reward += reward
time.sleep(0.1)
if done:
print(f'Score: {total_reward}, steps: {step}')
break
return