import numpy as np import tensorflow as tf import gym import time from spinup.algos.tf1.td3 import core from spinup.algos.tf1.td3.td3 import td3 as true_td3 from spinup.algos.tf1.td3.core import get_vars from spinup.utils.logx import EpochLogger """ Exercise 1.3: TD3 Computation Graph Implement the core computation graph for the TD3 algorithm. As starter code, you are given the entirety of the TD3 algorithm except for the computation graph. Find "YOUR CODE HERE" to begin. To clarify: you will not write an "actor_critic" function for this exercise. But you will use one to build the graph for computing the TD3 updates. """ class ReplayBuffer: """ A simple FIFO experience replay buffer for TD3 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]) def td3(env_fn, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0, steps_per_epoch=4000, epochs=100, replay_size=int(1e6), gamma=0.99, polyak=0.995, pi_lr=1e-3, q_lr=1e-3, batch_size=100, start_steps=10000, update_after=1000, update_every=50, act_noise=0.1, target_noise=0.2, noise_clip=0.5, policy_delay=2, num_test_episodes=10, max_ep_len=1000, logger_kwargs=dict(), save_freq=1): """ Twin Delayed Deep Deterministic Policy Gradient (TD3) 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 =========== ================ ====================================== ``pi`` (batch, act_dim) | Deterministically computes actions | from policy given states. ``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)). =========== ================ ====================================== ac_kwargs (dict): Any kwargs appropriate for the actor_critic function you provided to TD3. 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.) pi_lr (float): Learning rate for policy. q_lr (float): Learning rate for Q-networks. batch_size (int): Minibatch size for SGD. start_steps (int): Number of steps for uniform-random action selection, before running real policy. Helps exploration. update_after (int): Number of env interactions to collect before starting to do gradient descent updates. Ensures replay buffer is full enough for useful updates. update_every (int): Number of env interactions that should elapse between gradient descent updates. Note: Regardless of how long you wait between updates, the ratio of env steps to gradient steps is locked to 1. act_noise (float): Stddev for Gaussian exploration noise added to policy at training time. (At test time, no noise is added.) target_noise (float): Stddev for smoothing noise added to target policy. noise_clip (float): Limit for absolute value of target policy smoothing noise. policy_delay (int): Policy will only be updated once every policy_delay times for each update of the Q-networks. num_test_episodes (int): Number of episodes to test the deterministic policy at the end of each epoch. 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 # Inputs to computation graph x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(obs_dim, act_dim, obs_dim, None, None) #=========================================================================# # # # All of your code goes in the space below. # # # #=========================================================================# # Main outputs from computation graph with tf.variable_scope('main'): ####################### # # # YOUR CODE HERE # # # ####################### # pi, q1, q2, q1_pi = pass # Target policy network with tf.variable_scope('target'): ####################### # # # YOUR CODE HERE # # # ####################### # pi_targ = pass # Target Q networks with tf.variable_scope('target', reuse=True): # Target policy smoothing, by adding clipped noise to target actions ####################### # # # YOUR CODE HERE # # # ####################### # Target Q-values, using action from smoothed target policy ####################### # # # YOUR CODE HERE # # # ####################### pass # Experience buffer replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=replay_size) # Count variables var_counts = tuple(core.count_vars(scope) for scope in ['main/pi', 'main/q1', 'main/q2', 'main']) print('\nNumber of parameters: \t pi: %d, \t q1: %d, \t q2: %d, \t total: %d\n'%var_counts) # Bellman backup for Q functions, using Clipped Double-Q targets ####################### # # # YOUR CODE HERE # # # ####################### # TD3 losses ####################### # # # YOUR CODE HERE # # # ####################### # pi_loss = # q1_loss = # q2_loss = # q_loss = #=========================================================================# # # # All of your code goes in the space above. # # # #=========================================================================# # Separate train ops for pi, q 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') parser.add_argument('--seed', '-s', type=int, default=0) parser.add_argument('--exp_name', type=str, default='ex13-td3') parser.add_argument('--use_soln', action='store_true') args = parser.parse_args() from spinup.utils.run_utils import setup_logger_kwargs logger_kwargs = setup_logger_kwargs(args.exp_name + '-' + args.env.lower(), args.seed) all_kwargs = dict( env_fn=lambda : gym.make(args.env), actor_critic=core.mlp_actor_critic, ac_kwargs=dict(hidden_sizes=[128,128]), max_ep_len=150, seed=args.seed, logger_kwargs=logger_kwargs, epochs=10 ) if args.use_soln: true_td3(**all_kwargs) else: td3(**all_kwargs)