def main(_):
with tf.Session() as sess:
env = gym.make(ENV_NAME)
np.random.seed(RANDOM_SEED)
tf.set_random_seed(RANDOM_SEED)
env.seed(RANDOM_SEED)
print(env.observation_space)
print(env.action_space)
state_dim = env.observation_space.shape[0]
try:
action_dim = env.action_space.shape[0]
action_bound = env.action_space.high
# Ensure action bound is symmetric
assert (env.action_space.high == -env.action_space.low)
discrete = False
print('Continuous Action Space')
except IndexError:
action_dim = env.action_space.n
action_bound = 1
discrete = True
print('Discrete Action Space')
actor = ActorNetwork(sess, state_dim, action_dim, action_bound,
ACTOR_LEARNING_RATE, TAU)
critic = CriticNetwork(sess, state_dim, action_dim,
CRITIC_LEARNING_RATE, TAU,
actor.get_num_trainable_vars())
noise = Noise(DELTA, SIGMA, OU_A, OU_MU)
reward = Reward(REWARD_FACTOR, GAMMA)
if GYM_MONITOR_EN:
if not RENDER_ENV:
env = Monitor(env,
MONITOR_DIR,
video_callable=False,
force=True)
else:
env = Monitor(env, MONITOR_DIR, force=True)
try:
train(sess, env, actor, critic, noise, reward, discrete)
except KeyboardInterrupt:
pass
if GYM_MONITOR_EN:
env.close()
if stateValues[maxValueAction]['value'] < stateValues[action]['value']:
maxValueAction = action
if stateValues[minCountAction]['count'] > stateValues[action]['count']:
minCountAction = action
# Compute the decay of the exploration
decayX = 0.5
decayY = 50
decay = max(-i_episode * decayX + decayY, 10 / (i_episode + 1))
if randint(0, 100) < decay:
explorationHistory[i_episode] += 1
return minCountAction
else:
return maxValueAction
nbEpisodes = 1000
stepsHistory = [0] * nbEpisodes
env = gym.make('LunarLander-v2')
env = Monitor(env, 'tmp/cart-pole', force=True)
for i in range(6):
print i
history = {} # 'state' ==> [{'count': int, 'value': float}]
explorationHistory = [0] * nbEpisodes
learn(nbEpisodes, i)
env.close()
# gym.upload('tmp/cart-pole', api_key='sk_QoYvL963TwnAqSJXZLOQ')
plt.plot(range(nbEpisodes), stepsHistory, range(nbEpisodes),
explorationHistory, range(nbEpisodes), [195] * nbEpisodes)
plt.ylabel('Number of rewards')
plt.show()
randomAgent.remember(state, action, reward, next_state, done)
state = next_state
if done:
break
print("Finish to full the random agent memory")
agent.memory = randomAgent.memory
randomAgent = None
env = Monitor(env, 'tmp/cart-pole-ddqn-2', force=True)
for e in range(EPISODES):
if DEBUG and e >= EPISODES - 10:
agent.stopExploration()
state = env.reset()
for time in range(500):
#env.render()
# act on one input (one state)
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
agent.remember(state, action, reward, next_state, done)
agent.replay(batch_size)
state = next_state
if done:
print("episode: {}/{}, score: {}, e: {:.2}"
.format(e, EPISODES, time, agent.epsilon))
break
env.close()