t = 0
track_r = []
while True:
# if RENDER: env.render()
env.render()
a = actor.choose_acton(s)
s_, r, done, info = env.step(a)
position, velocity = s_
r = abs(position + 0.46)
track_r.append(r)
td_error = critic.learn(s, r, s_)
actor.learn(s, a, td_error)
s = s_
t += 1
if done:
ep_rs_sum = sum(track_r)
if 'running_reward' not in globals():
running_reward = ep_rs_sum
else:
running_reward = running_reward * 0.95 + ep_rs_sum * 0.05
if running_reward > 200:
RENDER = True
print("episode: ", i_episode, " reward: ", int(running_reward))
action = actor.choose_action(observation)
running_reward = 0
critic.reset()
count = 0
while count < Tmax:
count += 1
if RENDER: env.render()
observation_, reward, done, info = env.step(
action) # reward = -1 in all cases
# print(action, reward, observation_)
running_reward += reward
if done:
Tmax = count
delta = critic.learn(observation, reward, observation_)
actor.learn(observation, action, delta)
print(i_episode, running_reward)
if running_reward > DISPLAY_REWARD_THRESHOLD and i_episode > 1900:
RENDER = True # rendering
break
else:
action_ = actor.choose_action(observation_)
delta = critic.learn(observation, reward, observation_)
actor.learn(observation, action, delta)
observation = observation_
action = action_