action = actor.act(obs0) # TODO add noise for exploration
obs1, reward, done, info = env.step(action)
replay_buffer.add(obs0.reshape(state_dim),
action.reshape(action_dim), reward, t,
obs1.reshape(state_dim))
if replay_buffer.size() > MINIBATCH_SIZE:
minibatch = replay_buffer.sample_batch(MINIBATCH_SIZE)
s0_batch, a_batch, r_batch, t_batch, s1_batch = minibatch
actor_target_batch = actor.predict_target(s1_batch)
q_target_batch = critic.predict_target(
np.hstack((s1_batch, actor_target_batch)))
target_batch = r_batch + GAMMA * q_target_batch
loss = critic.learn(np.hstack((s0_batch, a_batch)),
target_batch)
# TODO update actor policy
actor.update_target()
critic.update_target()
obs0 = obs1
ep_reward += reward[0]
if done:
break
total += ep_reward
print("Episode {0:8d}: {1:4d} timesteps, {2:4f} average".format(
episode, t, total / (episode + 1)))
print(loss)
