spinup/algos/tf1/td3/td3.py [199:318]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr) q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr) train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi')) train_q_op = q_optimizer.minimize(q_loss, var_list=get_vars('main/q')) # Polyak averaging for target variables 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'))]) # 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) # Setup model saving logger.setup_tf_saver(sess, inputs={'x': x_ph, 'a': a_ph}, outputs={'pi': pi, 'q1': q1, 'q2': q2}) def get_action(o, noise_scale): a = sess.run(pi, feed_dict={x_ph: o.reshape(1,-1)})[0] a += noise_scale * np.random.randn(act_dim) return np.clip(a, -act_limit, act_limit) def test_agent(): for j in range(num_test_episodes): o, d, ep_ret, ep_len = test_env.reset(), False, 0, 0 while not(d or (ep_len == max_ep_len)): # Take deterministic actions at test time (noise_scale=0) o, r, d, _ = test_env.step(get_action(o, 0)) ep_ret += r ep_len += 1 logger.store(TestEpRet=ep_ret, TestEpLen=ep_len) start_time = time.time() o, ep_ret, ep_len = env.reset(), 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 (with some noise, via act_noise). if t > start_steps: a = get_action(o, act_noise) 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 # End of trajectory handling if d or (ep_len == max_ep_len): logger.store(EpRet=ep_ret, EpLen=ep_len) o, ep_ret, ep_len = env.reset(), 0, 0 # Update handling if t >= update_after and t % update_every == 0: for j in range(update_every): 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'] } q_step_ops = [q_loss, q1, q2, train_q_op] outs = sess.run(q_step_ops, feed_dict) logger.store(LossQ=outs[0], Q1Vals=outs[1], Q2Vals=outs[2]) if j % policy_delay == 0: # Delayed policy update outs = sess.run([pi_loss, train_pi_op, target_update], feed_dict) logger.store(LossPi=outs[0]) # End of epoch wrap-up if (t+1) % steps_per_epoch == 0: epoch = (t+1) // steps_per_epoch # Save model if (epoch % save_freq == 0) or (epoch == epochs): 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('LossPi', average_only=True) logger.log_tabular('LossQ', 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='HalfCheetah-v2') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - spinup/exercises/tf1/problem_set_1/exercise1_3.py [253:372]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr) q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr) train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi')) train_q_op = q_optimizer.minimize(q_loss, var_list=get_vars('main/q')) # Polyak averaging for target variables 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'))]) # 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) # Setup model saving logger.setup_tf_saver(sess, inputs={'x': x_ph, 'a': a_ph}, outputs={'pi': pi, 'q1': q1, 'q2': q2}) def get_action(o, noise_scale): a = sess.run(pi, feed_dict={x_ph: o.reshape(1,-1)})[0] a += noise_scale * np.random.randn(act_dim) return np.clip(a, -act_limit, act_limit) def test_agent(): for j in range(num_test_episodes): o, d, ep_ret, ep_len = test_env.reset(), False, 0, 0 while not(d or (ep_len == max_ep_len)): # Take deterministic actions at test time (noise_scale=0) o, r, d, _ = test_env.step(get_action(o, 0)) ep_ret += r ep_len += 1 logger.store(TestEpRet=ep_ret, TestEpLen=ep_len) start_time = time.time() o, ep_ret, ep_len = env.reset(), 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 (with some noise, via act_noise). if t > start_steps: a = get_action(o, act_noise) 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 # End of trajectory handling if d or (ep_len == max_ep_len): logger.store(EpRet=ep_ret, EpLen=ep_len) o, ep_ret, ep_len = env.reset(), 0, 0 # Update handling if t >= update_after and t % update_every == 0: for j in range(update_every): 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'] } q_step_ops = [q_loss, q1, q2, train_q_op] outs = sess.run(q_step_ops, feed_dict) logger.store(LossQ=outs[0], Q1Vals=outs[1], Q2Vals=outs[2]) if j % policy_delay == 0: # Delayed policy update outs = sess.run([pi_loss, train_pi_op, target_update], feed_dict) logger.store(LossPi=outs[0]) # End of epoch wrap-up if (t+1) % steps_per_epoch == 0: epoch = (t+1) // steps_per_epoch # Save model if (epoch % save_freq == 0) or (epoch == epochs): 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('LossPi', average_only=True) logger.log_tabular('LossQ', 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='HalfCheetah-v2') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -