cppo_agent.py

import time

import numpy as np
import tensorflow as tf
from baselines.common import explained_variance
from baselines.common.mpi_moments import mpi_moments
from baselines.common.running_mean_std import RunningMeanStd
from mpi4py import MPI
from mpi_utils import MpiAdamOptimizer
from rollouts import Rollout
from utils import bcast_tf_vars_from_root, get_mean_and_std
from vec_env import ShmemVecEnv as VecEnv

getsess = tf.get_default_session


class PpoOptimizer(object):
    envs = None

    def __init__(self, *, scope, ob_space, ac_space, stochpol, ent_coef, gamma, lam, nepochs, lr, cliprange,
                 nminibatches, normrew, normadv, use_news, ext_coeff, int_coeff, nsteps_per_seg, nsegs_per_env,
                 dynamic_bottleneck):
        self.dynamic_bottleneck = dynamic_bottleneck
        with tf.variable_scope(scope):
            self.use_recorder = True
            self.n_updates = 0
            self.scope = scope
            self.ob_space = ob_space    # Box(84,84,4)
            self.ac_space = ac_space    # Discrete(4)
            self.stochpol = stochpol    # cnn policy 
            self.nepochs = nepochs      # 3
            self.lr = lr                # 1e-4
            self.cliprange = cliprange  # 0.1
            self.nsteps_per_seg = nsteps_per_seg    # 128
            self.nsegs_per_env = nsegs_per_env      # 1
            self.nminibatches = nminibatches        # 8
            self.gamma = gamma                      # 0.99 
            self.lam = lam                          # 0.99 
            self.normrew = normrew                  # 1
            self.normadv = normadv                  # 1
            self.use_news = use_news                # False
            self.ext_coeff = ext_coeff              # 0.0
            self.int_coeff = int_coeff              # 1.0
            self.ph_adv = tf.placeholder(tf.float32, [None, None])
            self.ph_ret = tf.placeholder(tf.float32, [None, None])
            self.ph_rews = tf.placeholder(tf.float32, [None, None])
            self.ph_oldnlp = tf.placeholder(tf.float32, [None, None])    # -log pi(a|s)
            self.ph_oldvpred = tf.placeholder(tf.float32, [None, None])
            self.ph_lr = tf.placeholder(tf.float32, [])
            self.ph_cliprange = tf.placeholder(tf.float32, [])
            neglogpac = self.stochpol.pd.neglogp(self.stochpol.ph_ac)   
            entropy = tf.reduce_mean(self.stochpol.pd.entropy())
            vpred = self.stochpol.vpred

            vf_loss = 0.5 * tf.reduce_mean((vpred - self.ph_ret) ** 2)
            ratio = tf.exp(self.ph_oldnlp - neglogpac)  # p_new / p_old
            negadv = - self.ph_adv
            pg_losses1 = negadv * ratio
            pg_losses2 = negadv * tf.clip_by_value(ratio, 1.0 - self.ph_cliprange, 1.0 + self.ph_cliprange)
            pg_loss_surr = tf.maximum(pg_losses1, pg_losses2)
            pg_loss = tf.reduce_mean(pg_loss_surr)
            ent_loss = (- ent_coef) * entropy     
            approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - self.ph_oldnlp)) 
            clipfrac = tf.reduce_mean(tf.to_float(tf.abs(pg_losses2 - pg_loss_surr) > 1e-6))

            self.total_loss = pg_loss + ent_loss + vf_loss
            self.to_report = {'tot': self.total_loss, 'pg': pg_loss, 'vf': vf_loss, 'ent': entropy, 'approxkl': approxkl, 'clipfrac': clipfrac}

            # add bai
            self.db_loss = None

    def start_interaction(self, env_fns, dynamic_bottleneck, nlump=2):
        self.loss_names, self._losses = zip(*list(self.to_report.items()))

        params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
        params_db = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="DB")
        print("***total params:", np.sum([np.prod(v.get_shape().as_list()) for v in params]))  # idf:10,172,133
        print("***DB params:", np.sum([np.prod(v.get_shape().as_list()) for v in params_db]))  # idf:10,172,133

        if MPI.COMM_WORLD.Get_size() > 1:
            trainer = MpiAdamOptimizer(learning_rate=self.ph_lr, comm=MPI.COMM_WORLD)
        else:
            trainer = tf.train.AdamOptimizer(learning_rate=self.ph_lr)
        gradsandvars = trainer.compute_gradients(self.total_loss, params)     # 计算梯度
        self._train = trainer.apply_gradients(gradsandvars)

        # Train DB
        # gradsandvars_db = trainer.compute_gradients(self.db_loss, params_db)
        # self._train_db = trainer.apply_gradients(gradsandvars_db)

        # Train DB with gradient clipping
        gradients_db, variables_db = zip(*trainer.compute_gradients(self.db_loss, params_db))
        gradients_db, self.norm_var = tf.clip_by_global_norm(gradients_db, 50.0)
        self._train_db = trainer.apply_gradients(zip(gradients_db, variables_db))

        if MPI.COMM_WORLD.Get_rank() == 0:
            getsess().run(tf.variables_initializer(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)))
        bcast_tf_vars_from_root(getsess(), tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES))

        self.all_visited_rooms = []
        self.all_scores = []
        self.nenvs = nenvs = len(env_fns)        # 128
        self.nlump = nlump                       # 1
        self.lump_stride = nenvs // self.nlump   # 128/1=128
        self.envs = [
            VecEnv(env_fns[l * self.lump_stride: (l + 1) * self.lump_stride], spaces=[self.ob_space, self.ac_space]) for
            l in range(self.nlump)]

        self.rollout = Rollout(ob_space=self.ob_space, ac_space=self.ac_space, nenvs=nenvs,
                               nsteps_per_seg=self.nsteps_per_seg,
                               nsegs_per_env=self.nsegs_per_env, nlumps=self.nlump,
                               envs=self.envs,
                               policy=self.stochpol,
                               int_rew_coeff=self.int_coeff,
                               ext_rew_coeff=self.ext_coeff,
                               record_rollouts=self.use_recorder,
                               dynamic_bottleneck=dynamic_bottleneck)

        self.buf_advs = np.zeros((nenvs, self.rollout.nsteps), np.float32)
        self.buf_rets = np.zeros((nenvs, self.rollout.nsteps), np.float32)

        # add bai. Dynamic Bottleneck Reward Normalization
        if self.normrew:
            self.rff = RewardForwardFilter(self.gamma)
            self.rff_rms = RunningMeanStd()

        self.step_count = 0
        self.t_last_update = time.time()
        self.t_start = time.time()

    def stop_interaction(self):
        for env in self.envs:
            env.close()

    def calculate_advantages(self, rews, use_news, gamma, lam):
        nsteps = self.rollout.nsteps
        lastgaelam = 0
        for t in range(nsteps - 1, -1, -1):  # nsteps-2 ... 0
            nextnew = self.rollout.buf_news[:, t + 1] if t + 1 < nsteps else self.rollout.buf_new_last
            if not use_news:
                nextnew = 0
            nextvals = self.rollout.buf_vpreds[:, t + 1] if t + 1 < nsteps else self.rollout.buf_vpred_last
            nextnotnew = 1 - nextnew
            delta = rews[:, t] + gamma * nextvals * nextnotnew - self.rollout.buf_vpreds[:, t]
            self.buf_advs[:, t] = lastgaelam = delta + gamma * lam * nextnotnew * lastgaelam
        self.buf_rets[:] = self.buf_advs + self.rollout.buf_vpreds

    def update(self):
        # add bai. use dynamic bottleneck
        if self.normrew:     
            rffs = np.array([self.rff.update(rew) for rew in self.rollout.buf_rews.T])
            rffs_mean, rffs_std, rffs_count = mpi_moments(rffs.ravel())
            self.rff_rms.update_from_moments(rffs_mean, rffs_std ** 2, rffs_count)
            rews = self.rollout.buf_rews / np.sqrt(self.rff_rms.var)   # shape=(128,128)
        else:
            rews = np.copy(self.rollout.buf_rews)

        self.calculate_advantages(rews=rews, use_news=self.use_news, gamma=self.gamma, lam=self.lam)

        info = dict(
            advmean=self.buf_advs.mean(),
            advstd=self.buf_advs.std(),
            retmean=self.buf_rets.mean(),
            retstd=self.buf_rets.std(),
            vpredmean=self.rollout.buf_vpreds.mean(),
            vpredstd=self.rollout.buf_vpreds.std(),
            ev=explained_variance(self.rollout.buf_vpreds.ravel(), self.buf_rets.ravel()),
            DB_rew=np.mean(self.rollout.buf_rews),          # add bai.
            DB_rew_norm=np.mean(rews),                      # add bai.
            recent_best_ext_ret=self.rollout.current_max
        )
        if self.rollout.best_ext_ret is not None:
            info['best_ext_ret'] = self.rollout.best_ext_ret

        if self.normadv:
            m, s = get_mean_and_std(self.buf_advs)
            self.buf_advs = (self.buf_advs - m) / (s + 1e-7)
        envsperbatch = (self.nenvs * self.nsegs_per_env) // self.nminibatches
        envsperbatch = max(1, envsperbatch)
        envinds = np.arange(self.nenvs * self.nsegs_per_env)

        def resh(x):
            if self.nsegs_per_env == 1:
                return x
            sh = x.shape
            return x.reshape((sh[0] * self.nsegs_per_env, self.nsteps_per_seg) + sh[2:])

        ph_buf = [
            (self.stochpol.ph_ac, resh(self.rollout.buf_acs)),
            (self.ph_rews, resh(self.rollout.buf_rews)),
            (self.ph_oldvpred, resh(self.rollout.buf_vpreds)),
            (self.ph_oldnlp, resh(self.rollout.buf_nlps)),
            (self.stochpol.ph_ob, resh(self.rollout.buf_obs)),  # numpy shape=(128,128,84,84,4)
            (self.ph_ret, resh(self.buf_rets)),                 # 
            (self.ph_adv, resh(self.buf_advs)),                 #
        ]
        ph_buf.extend([
            (self.dynamic_bottleneck.last_ob,                   # shape=(128,1,84,84,4)
             self.rollout.buf_obs_last.reshape([self.nenvs * self.nsegs_per_env, 1, *self.ob_space.shape]))
        ])
        mblossvals = []                         # 
        for _ in range(self.nepochs):           # nepochs = 3
            np.random.shuffle(envinds)          # envinds = [0,1,2,...,127]
            # nenvs=128, nsgs_per_env=1, envsperbatch=16 
            for start in range(0, self.nenvs * self.nsegs_per_env, envsperbatch):
                end = start + envsperbatch
                mbenvinds = envinds[start:end]
                fd = {ph: buf[mbenvinds] for (ph, buf) in ph_buf}  # feed_dict
                fd.update({self.ph_lr: self.lr, self.ph_cliprange: self.cliprange})   # , self.dynamic_bottleneck.l2_aux_loss_tf: l2_aux_loss_fd})
                mblossvals.append(getsess().run(self._losses + (self._train,), fd)[:-1])  # 

                # gradient norm computation
                # print("gradient norm:", getsess().run(self.norm_var, fd))

            # momentum update DB parameters
            print("Momentum Update DB Encoder")
            getsess().run(self.dynamic_bottleneck.momentum_updates)
        DB_loss_info = getsess().run(self.dynamic_bottleneck.loss_info, fd)

        #
        mblossvals = [mblossvals[0]]
        info.update(zip(['opt_' + ln for ln in self.loss_names], np.mean([mblossvals[0]], axis=0)))
        info["rank"] = MPI.COMM_WORLD.Get_rank()
        self.n_updates += 1
        info["n_updates"] = self.n_updates
        info.update({dn: (np.mean(dvs) if len(dvs) > 0 else 0) for (dn, dvs) in self.rollout.statlists.items()})
        info.update(self.rollout.stats)
        if "states_visited" in info:
            info.pop("states_visited")
        tnow = time.time()
        info["ups"] = 1. / (tnow - self.t_last_update)
        info["total_secs"] = tnow - self.t_start
        info['tps'] = MPI.COMM_WORLD.Get_size() * self.rollout.nsteps * self.nenvs / (tnow - self.t_last_update)
        self.t_last_update = tnow

        return info, DB_loss_info

    def step(self):
        self.rollout.collect_rollout()                  
        update_info, DB_loss_info = self.update()       
        return {'update': update_info, "DB_loss_info": DB_loss_info}

    def get_var_values(self):
        return self.stochpol.get_var_values()

    def set_var_values(self, vv):
        self.stochpol.set_var_values(vv)


class RewardForwardFilter(object):
    def __init__(self, gamma):
        self.rewems = None
        self.gamma = gamma

    def update(self, rews):
        if self.rewems is None:
            self.rewems = rews
        else:
            self.rewems = self.rewems * self.gamma + rews
        return self.rewems