def train(sess, env, args, actor, critic, actor_noise, reward_result):
# Set up summary Ops
summary_ops, summary_vars = build_summaries()
sess.run(tf.global_variables_initializer())
# Get dynamics and initialize prior controller
[A, B] = get_linear_dynamics()
prior = BasePrior(A, B)
# Initialize target network weights
actor.update_target_network()
critic.update_target_network()
# Initialize replay memory
replay_buffer = ReplayBuffer(int(args['buffer_size']),
int(args['random_seed']))
paths = list()
for i in range(int(args['max_episodes'])):
s = env.reset()
ep_reward = 0.
ep_ave_max_q = 0
obs, action, rewards = [], [], []
#Get optimal reward using optimal control
s0 = np.copy(s)
ep_reward_opt = 0.
for kk in range(int(args['max_episode_len'])):
a_prior = prior.getControl_h(s0)
a = a_prior
s0, r, stop_c, _ = env.step(a)
ep_reward_opt += r
if (stop_c):
break
# Get reward using regRL algorithm
env.reset()
s = env.unwrapped.reset(s)
for j in range(int(args['max_episode_len'])):
# Set control prior regularization weight
lambda_mix = 5.
# Prior control
a_prior = prior.getControl_h(s)
# Rl control with exploration noise
a = actor.predict(np.reshape(s, (1, actor.s_dim))) + actor_noise()
#a = actor.predict(np.reshape(s, (1, actor.s_dim))) + (1. / (1. + i))
# Mix the actions (RL controller + control prior)
act = a[0] / (1 + lambda_mix) + (lambda_mix /
(1 + lambda_mix)) * a_prior
# Take action and observe next state/reward
s2, r, terminal, info = env.step(act)
# Add info from time step to the replay buffer
replay_buffer.add(
np.reshape(s, (actor.s_dim, )), np.reshape(a, (actor.a_dim, )),
r, terminal, np.reshape(s2, (actor.s_dim, )),
np.reshape((lambda_mix / (1 + lambda_mix)) * a_prior,
(actor.a_dim, )))
# Keep adding experience to the memory until
# there are at least minibatch size samples
if replay_buffer.size() > int(args['minibatch_size']):
#Sample a batch from the replay buffer
s_batch, a_batch_0, r_batch, t_batch, s2_batch, a_prior_batch = \
replay_buffer.sample_batch(int(args['minibatch_size']))
a_batch = a_batch_0
# Calculate targets
target_q = critic.predict_target(
s2_batch, actor.predict_target(s2_batch))
y_i = []
for k in range(int(args['minibatch_size'])):
if t_batch[k]:
y_i.append(r_batch[k])
else:
y_i.append(r_batch[k] + critic.gamma * target_q[k])
# Update the critic given the targets
predicted_q_value, _ = critic.train(
s_batch, a_batch,
np.reshape(y_i, (int(args['minibatch_size']), 1)))
ep_ave_max_q += np.amax(predicted_q_value)
# Update the actor policy using the sampled gradient
a_outs = actor.predict(s_batch)
grads = critic.action_gradients(s_batch, a_outs)
actor.train(s_batch, grads[0])
# Update target networks
actor.update_target_network()
critic.update_target_network()
# Calculate TD-Error for each state
base_q = critic.predict_target(s_batch,
actor.predict_target(s_batch))
target_q = critic.predict_target(
s2_batch, actor.predict_target(s2_batch))
s = s2
ep_reward += r
obs.append(s)
rewards.append(r)
action.append(a[0])
# Collect results at end of episode
if terminal:
for ii in range(len(obs)):
obs[ii] = obs[ii].reshape((4, 1))
print('| Reward: {:d} | Episode: {:d} | Qmax: {:.4f}'.format(int(ep_reward - ep_reward_opt), \
i, (ep_ave_max_q / float(j))))
reward_result[0, i] = ep_reward
reward_result[1, i] = ep_reward_opt
path = {
"Observation": np.concatenate(obs).reshape((-1, 4)),
"Action": np.concatenate(action),
"Reward": np.asarray(rewards)
}
paths.append(path)
print(ep_reward)
break
return [summary_ops, summary_vars, paths]
# Initialize control prior
[A,B] = get_linear_dynamics()
prior = BasePrior(A,B)
# Set fixed regularization weight
# lambda_mix = 4.
reward_total, reward_diff, reward_lqr_prior, reward_h_prior = [], [], [], []
for episode in range(EP_MAX + 1):
# Baseline reward using only control prior
s0 = env.reset()
sp = np.copy(s0)
reward_prior = 0.
while True:
a_prior = prior.getControl_h(sp)
a_prior = np.squeeze(np.asarray(a_prior))
sp, reward_p, done_p, _ = env.step(a_prior)
reward_prior += reward_p
if done_p:
break
env.reset()
sp = env.unwrapped.reset(s0)
reward_lqr = 0.
while True:
a_lqr = prior.getControl(sp)
a_lqr = np.squeeze(np.asarray(a_lqr))
sp, reward_p, done_p, _ = env.step(a_lqr)
reward_lqr += reward_p