spinningup给新手提供了几个重要算法的实现，具有很好的参考价值。除了SAC外，其他on policy算法都使用MPI进行并行化，唯独SAC没有并行实现。所以，我们使用Ray来完成SAC的并行实现。
这一节内容很简单，我们将spinningup里实现的sac分解开。在下一节，我们将分解开的每一个部分放入并行框架的对应位置。
我们的并行框架结构图：
我们根据我们的并行框架将sac分解为下面五个部分：
- Replay buffer
- Parameter server
- train (learn)
- rollout
- test
下面用注释将每一部分标注。
import numpy as np
import tensorflow as tf
import gym
import time
from spinup.algos.sac import core
from spinup.algos.sac.core import get_vars
from spinup.utils.logx import EpochLogger
# ********************** replaybuffer part below **********************
class ReplayBuffer:
"""
A simple FIFO experience replay buffer for SAC agents.
"""
def __init__(self, obs_dim, act_dim, size):
self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)
self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)
self.acts_buf = np.zeros([size, act_dim], dtype=np.float32)
self.rews_buf = np.zeros(size, dtype=np.float32)
self.done_buf = np.zeros(size, dtype=np.float32)
self.ptr, self.size, self.max_size = 0, 0, size
def store(self, obs, act, rew, next_obs, done):
self.obs1_buf[self.ptr] = obs
self.obs2_buf[self.ptr] = next_obs
self.acts_buf[self.ptr] = act
self.rews_buf[self.ptr] = rew
self.done_buf[self.ptr] = done
self.ptr = (self.ptr + 1) % self.max_size
self.size = min(self.size + 1, self.max_size)
def sample_batch(self, batch_size=32):
idxs = np.random.randint(0, self.size, size=batch_size)
return dict(obs1=self.obs1_buf[idxs],
obs2=self.obs2_buf[idxs],
acts=self.acts_buf[idxs],
rews=self.rews_buf[idxs],
done=self.done_buf[idxs])
# ********************** replaybuffer part above **********************
"""
Soft Actor-Critic
(With slight variations that bring it closer to TD3)
"""
def sac(env_fn, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
steps_per_epoch=5000, epochs=100, replay_size=int(1e6), gamma=0.99,
polyak=0.995, lr=1e-3, alpha=0.2, batch_size=100, start_steps=10000,
max_ep_len=1000, logger_kwargs=dict(), save_freq=1):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``mu`` (batch, act_dim) | Computes mean actions from policy
| given states.
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``. Critical: must be differentiable
| with respect to policy parameters all
| the way through action sampling.
``q1`` (batch,) | Gives one estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q2`` (batch,) | Gives another estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q1_pi`` (batch,) | Gives the composition of ``q1`` and
| ``pi`` for states in ``x_ph``:
| q1(x, pi(x)).
``q2_pi`` (batch,) | Gives the composition of ``q2`` and
| ``pi`` for states in ``x_ph``:
| q2(x, pi(x)).
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``.
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to SAC.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
lr (float): Learning rate (used for both policy and value learning).
alpha (float): Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.)
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
# logger = EpochLogger(**logger_kwargs)
# logger.save_config(locals())
tf.set_random_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# ********************** model part below **********************
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(obs_dim, act_dim, obs_dim, None, None)
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, logp_pi, q1, q2, q1_pi, q2_pi, v = actor_critic(x_ph, a_ph, **ac_kwargs)
# Target value network
with tf.variable_scope('target'):
_, _, _, _, _, _, _, v_targ = actor_critic(x2_ph, a_ph, **ac_kwargs)
# ********************** model part above **********************
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=replay_size)
# ********************** model part below **********************
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in
['main/pi', 'main/q1', 'main/q2', 'main/v', 'main'])
print(('\nNumber of parameters: \t pi: %d, \t' + 'q1: %d, \t q2: %d, \t v: %d, \t total: %d\n') % var_counts)
# Min Double-Q:
min_q_pi = tf.minimum(q1_pi, q2_pi)
# Targets for Q and V regression
q_backup = tf.stop_gradient(r_ph + gamma * (1 - d_ph) * v_targ)
v_backup = tf.stop_gradient(min_q_pi - alpha * logp_pi)
# Soft actor-critic losses
pi_loss = tf.reduce_mean(alpha * logp_pi - q1_pi)
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1) ** 2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2) ** 2)
v_loss = 0.5 * tf.reduce_mean((v_backup - v) ** 2)
value_loss = q1_loss + q2_loss + v_loss
# Policy train op
# (has to be separate from value train op, because q1_pi appears in pi_loss)
pi_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'))
# Value train op
# (control dep of train_pi_op because sess.run otherwise evaluates in nondeterministic order)
value_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
value_params = get_vars('main/q') + get_vars('main/v')
with tf.control_dependencies([train_pi_op]):
train_value_op = value_optimizer.minimize(value_loss, var_list=value_params)
# Polyak averaging for target variables
# (control flow because sess.run otherwise evaluates in nondeterministic order)
with tf.control_dependencies([train_value_op]):
target_update = tf.group([tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
# All ops to call during one training step
step_ops = [pi_loss, q1_loss, q2_loss, v_loss, q1, q2, v, logp_pi,
train_pi_op, train_value_op, target_update]
# Initializing targets to match main variables
target_init = tf.group([tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# ********************** model part above **********************
# Setup model saving
# logger.setup_tf_saver(sess, inputs={'x': x_ph, 'a': a_ph},
# outputs={'mu': mu, 'pi': pi, 'q1': q1, 'q2': q2, 'v': v})
def get_action(o, deterministic=False):
act_op = mu if deterministic else pi
return sess.run(act_op, feed_dict={x_ph: o.reshape(1, -1)})[0]
def test_agent(n=10):
global sess, mu, pi, q1, q2, q1_pi, q2_pi
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
o, r, d, _ = test_env.step(get_action(o, True))
ep_ret += r
ep_len += 1
print(ep_len, ep_ret)
# logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
# ********************** rollout part below **********************
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy.
"""
if t > start_steps:
a = get_action(o)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if d or (ep_len == max_ep_len):
"""
Perform all SAC updates at the end of the trajectory.
This is a slight difference from the SAC specified in the
original paper.
"""
# ********************** train part below **********************
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
}
outs = sess.run(step_ops, feed_dict)
# logger.store(LossPi=outs[0], LossQ1=outs[1], LossQ2=outs[2],
# LossV=outs[3], Q1Vals=outs[4], Q2Vals=outs[5],
# VVals=outs[6], LogPi=outs[7])
# ********************** train part above **********************
# logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# ********************** rollout part above **********************
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# Save model
# if (epoch % save_freq == 0) or (epoch == epochs - 1):
# logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
# logger.log_tabular('Epoch', epoch)
# logger.log_tabular('EpRet', with_min_and_max=True)
# logger.log_tabular('TestEpRet', with_min_and_max=True)
# logger.log_tabular('EpLen', average_only=True)
# logger.log_tabular('TestEpLen', average_only=True)
# logger.log_tabular('TotalEnvInteracts', t)
# logger.log_tabular('Q1Vals', with_min_and_max=True)
# logger.log_tabular('Q2Vals', with_min_and_max=True)
# logger.log_tabular('VVals', with_min_and_max=True)
# logger.log_tabular('LogPi', with_min_and_max=True)
# logger.log_tabular('LossPi', average_only=True)
# logger.log_tabular('LossQ1', average_only=True)
# logger.log_tabular('LossQ2', average_only=True)
# logger.log_tabular('LossV', average_only=True)
# logger.log_tabular('Time', time.time() - start_time)
# logger.dump_tabular()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BipedalWalker-v2')
parser.add_argument('--hid', type=int, default=300)
parser.add_argument('--l', type=int, default=1)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--seed', '-s', type=int, default=0)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--exp_name', type=str, default='sac')
args = parser.parse_args()
# from spinup.utils.run_utils import setup_logger_kwargs
#
# logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed)
sac(lambda: gym.make(args.env), actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(hidden_sizes=[args.hid] * args.l),
gamma=args.gamma, seed=args.seed, epochs=args.epochs,)
# logger_kwargs=logger_kwargs)本节完。