class DDPG():
def __init__(self, parameters):
self.parameters = parameters
self.env = gym.make(
self.parameters['env'][:self.parameters['env'].find('_')])
self.nA = self.env.action_space.sample().shape[0]
self.state_size = self.env.reset().shape[0]
# Build our replay memory
self.memory = Memory(replay_size=self.parameters['replay_size'],
action_size=self.nA,
state_size=self.state_size,
batch_size=self.parameters['batch_size'])
# Create actor and critic
self.actor_critic = ActorCritic(
actor_lr=parameters['actor_learning_rate'],
critic_lr=parameters['critic_learning_rate'],
gamma=parameters['gamma'],
state_size=self.state_size,
action_size=self.nA,
tau=parameters['tau'])
def train(self):
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = True
# Create global step and increment operation
global_step_tensor = tf.Variable(0,
trainable=False,
name='global_step')
increment_global_step = tf.assign_add(global_step_tensor, 1)
# Create model saver
saver = tf.train.Saver(max_to_keep=None)
sess = tf.Session(config=config)
if not self.parameters['restore']:
sess.run(tf.global_variables_initializer())
else:
saver.restore(sess, tf.train.latest_checkpoint('./saves'))
self.actor_critic.set_moving_to_target(sess)
run_id = np.random.randint(10000)
trainwriter = tf.summary.FileWriter(logdir='./logs/' + str(run_id),
graph=sess.graph)
# Get action noise
action_noise = OrnsteinUhlenbeckActionNoise(
mu=np.zeros(self.nA),
sigma=float(self.parameters['sigma']) * np.ones(self.nA))
# Fill Replay Memory
state = self.env.reset()
fill_amount = 0
while fill_amount < self.parameters['replay_init_size']:
action = self.env.action_space.sample()
next_state, reward, done, _ = self.env.step(action)
if done:
state = self.env.reset()
else:
fill_amount += 1
self.memory.add(state, action, reward, done, next_state)
state = next_state
# Main Loop
plots = {'critic_loss': [], 'actor_loss': [], 'episode_reward': []}
plots_dir = './plots/'
weights_dir = './weights/'
graph_dir = './graph/'
if not os.path.exists(plots_dir):
os.makedirs(plots_dir)
if not os.path.exists(weights_dir):
os.makedirs(weights_dir)
if not os.path.exists(graph_dir):
os.makedirs(graph_dir)
saver.export_meta_graph(graph_dir + self.parameters['env'] +
'/graph.meta')
#cumulative step counter
cumu_step = 0
for i in range(self.parameters['num_epochs']):
avg_epoch_rewards = 0
n_epochs = 1
for e in range(self.parameters['num_episodes']):
state = self.env.reset()
ep_reward = 0
ep_n_action = 0
# Perform rollout
for _ in range(500):
noise = action_noise()
action = self.actor_critic.pi(sess, state[None, ...])
action += noise
action = np.clip(action, self.env.action_space.low[0],
self.env.action_space.high[0])
assert action.shape == self.env.action_space.shape
next_state, reward, done, _ = self.env.step(action)
# print(action)
# print(next_state)
# print(reward)
self.memory.add(state, action, reward, done, next_state)
if self.parameters['render_train']: self.env.render()
ep_reward += reward
ep_n_action += 1
cumu_step += 1
state = next_state
# Perform train
avg_critic_loss = 0.0
avg_actor_loss = 0.0
for t in range(self.parameters['num_train_steps']):
s_state, s_action, s_reward, s_next_state, s_terminal = self.memory.sample(
)
# Train actor critic model
_, _, critic_loss, actor_loss = self.actor_critic.update(
sess=sess,
filewriter=trainwriter,
state_batch=s_state,
next_state_batch=s_next_state,
action_batch=s_action,
reward_batch=s_reward,
done_batch=s_terminal)
avg_critic_loss += critic_loss
avg_actor_loss += actor_loss
sess.run(increment_global_step)
avg_critic_loss /= self.parameters['num_train_steps']
avg_actor_loss /= self.parameters['num_train_steps']
if done:
reward_summary = tf.Summary(value=[
tf.Summary.Value(tag="ep_rewards",
simple_value=ep_reward)
])
trainwriter.add_summary(
reward_summary,
i * self.parameters['num_episodes'] + e)
action_noise.reset()
break
avg_epoch_rewards = avg_epoch_rewards + (
ep_reward - avg_epoch_rewards) / n_epochs
n_epochs += 1
print('Epoch: {:d} | Reward: {:d} | Avg_Q_loss: {:.4f} | Avg_a_loss: {:.4f} | Episode: {:d} | Step: {:d} | Cumu Step: {:d}'\
.format(i+1, int(ep_reward), avg_critic_loss, avg_actor_loss, e+1, ep_n_action, cumu_step))
if e % 19 == 0:
save_path = saver.save(
sess,
weights_dir + self.parameters['env'] + '/model.ckpt',
global_step=i * e + 1)
plots['episode_reward'].append(ep_reward)
plots['critic_loss'].append(critic_loss)
plots['actor_loss'].append(critic_loss)
pickle.dump(
plots,
open(plots_dir + self.parameters['env'] + '_plot.pickle',
'wb'))
def test(self):
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = True
saver = tf.train.Saver()
sess = tf.Session(config=config)
saver.restore(
sess,
tf.train.latest_checkpoint(
'./weights/HalfCheetah-v2_kirkiles_train50episode_noise_norm_bufsize1Mi1k'
))
while True:
state = self.env.reset()
# Perform rollout
while True:
action = self.actor_critic.pi(sess, state[None, ...])
action = np.clip(action, self.env.action_space.low[0],
self.env.action_space.high[0])
#print(action)
assert action.shape == self.env.action_space.shape
next_state, reward, done, _ = self.env.step(action)
self.env.render()
if done:
break
state = next_state
class DDPG():
def __init__(self, parameters):
self.parameters = parameters
self.env = gym.make(self.parameters['env'])
self.nA = self.env.action_space.sample().shape[0]
self.state_size = self.env.reset().shape[0]
# Build our replay memory
self.memory = Memory(replay_size=self.parameters['replay_size'],
action_size=self.nA,
state_size=self.state_size,
batch_size=self.parameters['batch_size'])
# Create actor and critic
self.actor_critic = ActorCritic(
actor_lr=parameters['actor_learning_rate'],
critic_lr=parameters['critic_learning_rate'],
gamma=parameters['gamma'],
state_size=self.state_size,
action_size=self.nA,
tau=parameters['tau'])
def train(self):
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = True
# Create global step and increment operation
global_step_tensor = tf.Variable(0,
trainable=False,
name='global_step')
increment_global_step = tf.assign_add(global_step_tensor, 1)
# Create model saver
saver = tf.train.Saver()
sess = tf.Session(config=config)
if not self.parameters['restore']:
sess.run(tf.global_variables_initializer())
else:
saver.restore(sess, tf.train.latest_checkpoint('./saves'))
self.actor_critic.set_moving_to_target(sess)
run_id = np.random.randint(10000)
trainwriter = tf.summary.FileWriter(logdir='./logs/' + str(run_id),
graph=sess.graph)
# Get action noise
action_noise = OrnsteinUhlenbeckActionNoise(
mu=np.zeros(self.nA),
sigma=float(self.parameters['sigma']) * np.ones(self.nA))
# Fill Replay Memory
state = self.env.reset()
fill_amount = 0
while fill_amount < self.parameters['replay_init_size']:
action = self.env.action_space.sample()
next_state, reward, done, _ = self.env.step(action)
if done:
state = self.env.reset()
else:
fill_amount += 1
self.memory.add(state, action, reward, done, next_state)
state = next_state
# Main Loop
steps = 0
for i in range(self.parameters['num_epochs']):
avg_epoch_rewards = 0
num_epochs = 1
for e in range(self.parameters['num_episodes']):
state = self.env.reset()
ep_reward = 0
# Perform rollout
while True:
noise = action_noise()
action = self.actor_critic.pi(sess, state[None, ...])
action += noise
action = np.clip(action, self.env.action_space.low[0],
self.env.action_space.high[0])
assert action.shape == self.env.action_space.shape
"""
# UNCOMMENT TO PRINT ACTIONS
a0 = tf.Summary(value=[tf.Summary.Value(tag="action_0", simple_value=action[0,0])])
trainwriter.add_summary(a0,steps)
a1 = tf.Summary(value=[tf.Summary.Value(tag="action_1", simple_value=action[0,1])])
trainwriter.add_summary(a1,steps)
a2 = tf.Summary(value=[tf.Summary.Value(tag="action_2", simple_value=action[0,2])])
trainwriter.add_summary(a2,steps)
steps += 1
"""
next_state, reward, done, _ = self.env.step(action)
self.memory.add(state, action, reward, done, next_state)
if self.parameters['render_train']:
self.env.render()
ep_reward += reward
if done:
reward_summary = tf.Summary(value=[
tf.Summary.Value(tag="ep_rewards",
simple_value=ep_reward)
])
trainwriter.add_summary(
reward_summary,
i * self.parameters['num_episodes'] + e)
action_noise.reset()
break
state = next_state
avg_epoch_rewards = avg_epoch_rewards + (
ep_reward - avg_epoch_rewards) / num_epochs
num_epochs += 1
# Perform train
for t in range(self.parameters['num_train_steps']):
s_state, s_action, s_reward, s_next_state, s_terminal = self.memory.sample(
)
# Train actor critic model
self.actor_critic.update(sess=sess,
filewriter=trainwriter,
state_batch=s_state,
next_state_batch=s_next_state,
action_batch=s_action,
reward_batch=s_reward,
done_batch=s_terminal)
sess.run(increment_global_step)
# Print out epoch stats here
table_data = [['Epoch', 'Average Reward'],
[
str(i) + "/" +
str(self.parameters['num_epochs']),
str(avg_epoch_rewards)
]]
table = AsciiTable(table_data, "Training Run: " + str(run_id))
save_path = saver.save(sess, "./saves/model.ckpt")
os.system('clear')
print("Model saved in path: %s" % save_path + "\n" + table.table)
def test(self):
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = True
saver = tf.train.Saver()
sess = tf.Session(config=config)
saver.restore(sess, tf.train.latest_checkpoint('./saves'))
while True:
state = self.env.reset()
# Perform rollout
while True:
action = self.actor_critic.pi(sess, state[None, ...])
action = np.clip(action, self.env.action_space.low[0],
self.env.action_space.high[0])
assert action.shape == self.env.action_space.shape
next_state, reward, done, _ = self.env.step(action)
self.env.render()
if done:
break
state = next_state
scaled = scaler.transform([state])
featurized = featurizer.transform(scaled)
return featurized[0]
ac = ActorCritic(featurize_state(observation_space).shape[0])
while True:
done = False
s = env.reset()
reward = []
while not done:
s = featurize_state(s)
#env.render()
action = ac.act(s)
s_prime, r, done, _ = env.step([action])
reward.append(r)
value_next = ac.value_estimate(featurize_state(s_prime)).detach()
td_target = r + gamma * value_next
td_error = td_target - ac.value_estimate(s).detach()
ac.update(s, td_target, td_error, action)
s = s_prime
print('Avg reward:', np.mean(reward), np.max(reward))
ac.plot()