train_episodes = 5000
avg_score = 0
loss = 0
for episode in range(train_episodes):
state = env.reset()
state = np.reshape(state, [1, state_size])
cum_reward = 0
for i in range(1000):
action = agent.act(state, is_training=True)
next_state, reward, done, _ = env.step(action)
next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward)
state = next_state
cum_reward += reward
if done:
avg_score += cum_reward
break
current_loss = agent.update()[0]
loss += current_loss
if episode % 100 == 0 and episode != 0:
print("Episode: " + str(episode) + "/" + str(train_episodes) + ", score: " + str(avg_score/100) + ", Loss : " + str(loss/100))
avg_score = 0
loss = 0
