def evolve(self, population, save=True):
"""
Evolve agents
:param population:
:type population:
:param save: save agents weights and scores
:type save: bool
:return:
"""
timestamp = datetime.now().strftime('%Y%m%d%H%M%S')
print("Optimization - started", timestamp)
agents = population.create_population()
for i in range(population.max_generations):
if not self.terminate(population, i):
try:
population.agents_weights[i] = np.array(
[a.model.get_weights() for a in agents],
dtype=np.ndarray)
for j, agent in enumerate(agents): # TODO parallelize
score = agent.run_agent()
population.scores[i][j] = score
print_scores(i + 1, population.scores[i])
if save and (i + 1) % 50 == 0:
save_results(population.agents_weights[:i],
population.scores[:i], timestamp)
if i < population.max_generations - 1:
self.generate_next_generation(population=population,
generation=i)
for k, a in enumerate(agents):
agents[k].model.set_weights(
population.agents_weights[i + 1][k])
except KeyboardInterrupt:
LOGGER.log(environment=ENVIRONMENT.name,
timestamp=timestamp,
algorithm=self.__class__.__name__,
parameters=vars(self),
generations=i,
score=np.max(population.scores[i - 1]))
save_results(population.agents_weights[:i - 1],
population.scores[:i - 1], timestamp)
sys.exit()
else:
population.agents_weights = population.agents_weights[:i]
population.scores = population.scores[:i]
break
if save:
LOGGER.log(environment=ENVIRONMENT.name,
timestamp=timestamp,
algorithm=self.__class__.__name__,
parameters=vars(self),
generations=i,
score=np.max(population.scores[i]))
save_results(population.agents_weights, population.scores,
timestamp)
return population.agents_weights, population.scores
def run(self, train=True):
"""
Run DDPG agent
:param train: train indicator
:type train: bool
:return:
"""
timestamp = datetime.now().strftime('%Y%m%d%H%M%S')
step = 0
self.load_weights()
try:
for i in range(self.n_episodes):
# get initial state
state = self.environment.env.reset()
total_reward = 0
for j in range(self.environment.max_time):
# loss = 0
self.environment.env.render()
action = self.actor.model.predict(
state.reshape(1, state.shape[0]))
action = self.noise.get_noisy_action(action, j)
new_state, reward, done, info = self.environment.env.step(
action[0])
# td_error = reward + self.gamma * 1 - 1
self.buffer.add(state, action[0], reward, new_state, done)
s, a, r, new_s, d = self.buffer.get_batch(self.batch_size)
target_q = self.critic.target_model.predict(
[new_s, self.actor.target_model.predict(new_s)])
y = r
for k in range(len(d)):
# if d[k]:
# y[k] = r[k]
# else:
# y[k] = r[k] + self.gamma * target_q[k]
y[k] = r[k] + self.gamma * target_q[k]
if train:
# loss += self.critic.model.train_on_batch([s, a], y)
self.critic.model.train_on_batch([s, a], y)
a_grads = self.actor.model.predict(s)
grads = self.critic.gradients(s, a_grads)
self.actor.train(s, grads)
self.actor.update_target()
self.critic.update_target()
total_reward += reward
if i + 1 % 200 == 0:
LOGGER.log(environment=self.environment.name,
timestamp=timestamp,
algorithm=self.__class__.__name__,
parameters=self.get_params(),
total_steps=i,
score=total_reward)
self.save_weights()
if done: # or np.array_equal(np.around(new_state, 3), np.around(state, 3)):
previous_reward = total_reward
break
state = new_state
# print("Episode", i, "Step", step, "Action", action, "Reward", reward, "Loss", loss)
step += 1
print(
"Episode: {:<5d} Total Reward: {:<+10.3f} Total Steps: {:<10d} "
" Replay Buffer size: {}".format(
i, total_reward, step, self.buffer.n_experiences))
except KeyboardInterrupt:
print("Training interrupted.")
print("Saving weights...")
self.save_weights()
def train(self):
"""
Train BipedaWaker-v2 agent
:return:
"""
# get start timestamp
timestamp = datetime.now().strftime('%Y%m%d%H%M%S')
# saving paths
model_path = os.path.join(td3_cfg.models_path,
'{}-{}'.format(env_cfg.name,
timestamp), 'model.ckpt')
results_path = os.path.join(td3_cfg.models_path,
'{}-{}'.format(env_cfg.name,
timestamp), 'results.npy')
distances_path = os.path.join(td3_cfg.models_path,
'{}-{}'.format(env_cfg.name, timestamp),
'distances.npy')
weights_path = os.path.join(td3_cfg.models_path,
'{}-{}'.format(env_cfg.name, timestamp),
'weights_init_fin.npy')
video_dir = os.path.join(td3_cfg.models_path,
'{}-{}'.format(env_cfg.name,
timestamp), 'video')
# read environment information from config
env = env_cfg.env
# record video every 100 episodes
if td3_cfg.record_videos:
env = wrappers.Monitor(
env,
video_dir,
video_callable=lambda ep: ep % td3_cfg.record_videos == 0)
# arrays with rewards, distances for later optimality analysis
rewards = []
distances_consecutive = np.zeros(2, dtype=np.ndarray)
distances_init = np.zeros(2, dtype=np.ndarray)
with tf.Session() as sess:
# initialization
self.agent = TD3(sess)
saver = tf.train.Saver()
init = tf.global_variables_initializer()
sess.run(init)
self.agent.initialize()
global_step = 0
weights_init = get_actor_weights(sess)
weights_old = weights_init
try:
for i in range(td3_cfg.n_episodes):
s = env.reset() # get initial state
ep_reward = 0
ep_steps = 0
noises = []
actions = []
done = False
while not done:
env.render()
if ep_steps < 10:
action = self.agent.get_random_action()
else:
action, action_org, noise = self.agent.get_noisy_action(
s)
noises.append(noise)
actions.append(action_org)
action = action.squeeze()
s2, r, done, info = env.step(action.tolist())
ep_reward += r
ep_steps += 1
global_step += 1
# store transition in replay buffer
self.agent.store_experience(s, action, r, done, s2)
# use symmetry of leg 1 and leg 2
mirrored_s = mirror_state(s)
mirrored_s2 = mirror_state(s2)
mirrored_a = mirror_action(action)
self.agent.store_experience(mirrored_s, mirrored_a, r,
done, mirrored_s2)
# train agent
temp = self.agent.train(global_step)
if temp:
weights = temp
s = s2
if done:
# end of the episode
count = i + 1
# get trained weights
weights = get_actor_weights(sess)
for iw, w in enumerate(weights):
# compute the distances with the previous weights and the initial weights
con, init = compute_distance_episodes(
weights_init[iw], weights_old[iw],
weights[iw])
distances_consecutive[iw] = np.append(
distances_consecutive[iw], con)
distances_init[iw] = np.append(
distances_init[iw], init)
weights_old = weights
# evaluation
if count % td3_cfg.test_every == 0:
eval_ep_reward, eval_ep_steps = self.evaluate(
env)
print(
"Episode: {:<10d} Evaluation Reward: {:<+10.3f} "
"Total Training Steps: {:10d}".format(
count, eval_ep_reward, global_step))
rewards.append(eval_ep_reward)
# saving
if count % td3_cfg.save_every == 0:
saver.save(sess, model_path, global_step=count)
np.save(results_path, rewards)
np.save(
distances_path,
np.vstack((distances_consecutive,
distances_init)))
np.save(weights_path,
np.append(weights_init, weights))
except KeyboardInterrupt:
print("Training interrupted.")
# Finalize training and save results
print('Total steps:', global_step)
print("Saving results...")
LOGGER.log(environment=env_cfg.name,
timestamp=timestamp,
algorithm=self.agent.__class__.__name__,
parameters=vars(td3_cfg),
total_steps=global_step,
score=eval_ep_reward)
saver.save(sess, model_path, global_step=count)
np.save(results_path, rewards)
np.save(distances_path,
np.vstack((distances_consecutive, distances_init)))
np.save(weights_path, np.append(weights_init, weights))
env.close()