spinup/exercises/tf1/problem_set_1/exercise1_3.py

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)