def kl(self, other): a0 = self.logits - U.max(self.logits, axis=1, keepdims=True) a1 = other.logits - U.max(other.logits, axis=1, keepdims=True) ea0 = tf.exp(a0) ea1 = tf.exp(a1) z0 = U.sum(ea0, axis=1, keepdims=True) z1 = U.sum(ea1, axis=1, keepdims=True) p0 = ea0 / z0 return U.sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), axis=1)
def kl(self, other): assert isinstance(other, DiagGaussianPd) return U.sum( other.logstd - self.logstd + (tf.square(self.std) + tf.square(self.mean - other.mean)) / (2.0 * tf.square(other.std)) - 0.5, axis=1)
def entropy(self): return U.sum(tf.nn.sigmoid_cross_entropy_with_logits( logits=self.logits, labels=self.ps), axis=1)
def kl(self, other): return U.sum(tf.nn.sigmoid_cross_entropy_with_logits( logits=other.logits, labels=self.ps), axis=1) - U.sum(tf.nn.sigmoid_cross_entropy_with_logits( logits=self.logits, labels=self.ps), axis=1)
def logp(self, x): return -U.sum(tf.nn.sigmoid_cross_entropy_with_logits( logits=self.logits, labels=tf.to_float(x)), axis=1)
def entropy(self): return U.sum(self.logstd + .5 * np.log(2.0 * np.pi * np.e), 1)
def logp(self, x): return - 0.5 * U.sum(tf.square((x - self.mean) / self.std), axis=1) \ - 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[1]) \ - U.sum(self.logstd, axis=1)
def entropy(self): a0 = self.logits - U.max(self.logits, axis=1, keepdims=True) ea0 = tf.exp(a0) z0 = U.sum(ea0, axis=1, keepdims=True) p0 = ea0 / z0 return U.sum(p0 * (tf.log(z0) - a0), axis=1)
def learn( env, policy_func, discriminator, expert_dataset, embedding_z, pretrained, pretrained_weight, *, g_step, d_step, timesteps_per_batch, # what to train on max_kl, cg_iters, gamma, lam, # advantage estimation entcoeff=0.0, cg_damping=1e-2, vf_stepsize=3e-4, d_stepsize=3e-4, vf_iters=3, max_timesteps=0, max_episodes=0, max_iters=0, # time constraint callback=None, save_per_iter=100, ckpt_dir=None, log_dir=None, load_model_path=None, task_name=None): nworkers = MPI.COMM_WORLD.Get_size() rank = MPI.COMM_WORLD.Get_rank() np.set_printoptions(precision=3) # Setup losses and stuff # ---------------------------------------- ob_space = env.observation_space ac_space = env.action_space pi = policy_func("pi", ob_space, ac_space, reuse=(pretrained_weight != None)) oldpi = policy_func("oldpi", ob_space, ac_space) atarg = tf.placeholder( dtype=tf.float32, shape=[None]) # Target advantage function (if applicable) ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return ob = U.get_placeholder_cached(name="ob") ac = pi.pdtype.sample_placeholder([None]) kloldnew = oldpi.pd.kl(pi.pd) ent = pi.pd.entropy() meankl = U.mean(kloldnew) meanent = U.mean(ent) entbonus = entcoeff * meanent vferr = U.mean(tf.square(pi.vpred - ret)) ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # advantage * pnew / pold surrgain = U.mean(ratio * atarg) optimgain = surrgain + entbonus losses = [optimgain, meankl, entbonus, surrgain, meanent] loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"] dist = meankl all_var_list = pi.get_trainable_variables() var_list = [ v for v in all_var_list if v.name.split("/")[1].startswith("pol") ] vf_var_list = [ v for v in all_var_list if v.name.split("/")[1].startswith("vf") ] d_adam = MpiAdam(discriminator.get_trainable_variables()) vfadam = MpiAdam(vf_var_list) get_flat = U.GetFlat(var_list) set_from_flat = U.SetFromFlat(var_list) klgrads = tf.gradients(dist, var_list) flat_tangent = tf.placeholder(dtype=tf.float32, shape=[None], name="flat_tan") shapes = [var.get_shape().as_list() for var in var_list] start = 0 tangents = [] for shape in shapes: sz = U.intprod(shape) tangents.append(tf.reshape(flat_tangent[start:start + sz], shape)) start += sz gvp = tf.add_n( [U.sum(g * tangent) for (g, tangent) in zipsame(klgrads, tangents)]) # pylint: disable=E1111 fvp = U.flatgrad(gvp, var_list) assign_old_eq_new = U.function( [], [], updates=[ tf.assign(oldv, newv) for (oldv, newv) in zipsame(oldpi.get_variables(), pi.get_variables()) ]) compute_losses = U.function([ob, ac, atarg], losses) compute_lossandgrad = U.function([ob, ac, atarg], losses + [U.flatgrad(optimgain, var_list)]) compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp) compute_vflossandgrad = U.function([ob, ret], U.flatgrad(vferr, vf_var_list)) @contextmanager def timed(msg): if rank == 0: print(colorize(msg, color='magenta')) tstart = time.time() yield print( colorize("done in %.3f seconds" % (time.time() - tstart), color='magenta')) else: yield def allmean(x): assert isinstance(x, np.ndarray) out = np.empty_like(x) MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM) out /= nworkers return out writer = U.FileWriter(log_dir) U.initialize() th_init = get_flat() MPI.COMM_WORLD.Bcast(th_init, root=0) set_from_flat(th_init) d_adam.sync() vfadam.sync() print("Init param sum", th_init.sum(), flush=True) # Prepare for rollouts # ---------------------------------------- seg_gen = traj_segment_generator(pi, env, discriminator, embedding=embedding_z, timesteps_per_batch=timesteps_per_batch, stochastic=True) episodes_so_far = 0 timesteps_so_far = 0 iters_so_far = 0 tstart = time.time() lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards true_rewbuffer = deque(maxlen=40) assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1 g_loss_stats = stats(loss_names) d_loss_stats = stats(discriminator.loss_name) ep_stats = stats(["True_rewards", "Rewards", "Episode_length"]) # if provide pretrained weight if pretrained_weight is not None: U.load_state(pretrained_weight, var_list=pi.get_variables()) # if provieded model path if load_model_path is not None: U.load_state(load_model_path) while True: if callback: callback(locals(), globals()) if max_timesteps and timesteps_so_far >= max_timesteps: break elif max_episodes and episodes_so_far >= max_episodes: break elif max_iters and iters_so_far >= max_iters: break # Save model if iters_so_far % save_per_iter == 0 and ckpt_dir is not None: U.save_state(os.path.join(ckpt_dir, task_name), counter=iters_so_far) logger.log("********** Iteration %i ************" % iters_so_far) def fisher_vector_product(p): return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p # ------------------ Update G ------------------ logger.log("Optimizing Policy...") for _ in range(g_step): with timed("sampling"): seg = seg_gen.__next__() add_vtarg_and_adv(seg, gamma, lam) # ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets)) ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[ "tdlamret"] vpredbefore = seg[ "vpred"] # predicted value function before udpate atarg = (atarg - atarg.mean()) / atarg.std( ) # standardized advantage function estimate if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy args = seg["ob"], seg["ac"], atarg fvpargs = [arr[::5] for arr in args] assign_old_eq_new( ) # set old parameter values to new parameter values with timed("computegrad"): *lossbefore, g = compute_lossandgrad(*args) lossbefore = allmean(np.array(lossbefore)) g = allmean(g) if np.allclose(g, 0): logger.log("Got zero gradient. not updating") else: with timed("cg"): stepdir = cg(fisher_vector_product, g, cg_iters=cg_iters, verbose=rank == 0) assert np.isfinite(stepdir).all() shs = .5 * stepdir.dot(fisher_vector_product(stepdir)) lm = np.sqrt(shs / max_kl) # logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g)) fullstep = stepdir / lm expectedimprove = g.dot(fullstep) surrbefore = lossbefore[0] stepsize = 1.0 thbefore = get_flat() for _ in range(10): thnew = thbefore + fullstep * stepsize set_from_flat(thnew) meanlosses = surr, kl, *_ = allmean( np.array(compute_losses(*args))) improve = surr - surrbefore logger.log("Expected: %.3f Actual: %.3f" % (expectedimprove, improve)) if not np.isfinite(meanlosses).all(): logger.log("Got non-finite value of losses -- bad!") elif kl > max_kl * 1.5: logger.log("violated KL constraint. shrinking step.") elif improve < 0: logger.log("surrogate didn't improve. shrinking step.") else: logger.log("Stepsize OK!") break stepsize *= .5 else: logger.log("couldn't compute a good step") set_from_flat(thbefore) if nworkers > 1 and iters_so_far % 20 == 0: paramsums = MPI.COMM_WORLD.allgather( (thnew.sum(), vfadam.getflat().sum())) # list of tuples assert all( np.allclose(ps, paramsums[0]) for ps in paramsums[1:]) with timed("vf"): for _ in range(vf_iters): for (mbob, mbret) in dataset.iterbatches( (seg["ob"], seg["tdlamret"]), include_final_partial_batch=False, batch_size=128): if hasattr(pi, "ob_rms"): pi.ob_rms.update( mbob) # update running mean/std for policy g = allmean(compute_vflossandgrad(mbob, mbret)) vfadam.update(g, vf_stepsize) g_losses = meanlosses for (lossname, lossval) in zip(loss_names, meanlosses): logger.record_tabular(lossname, lossval) logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret)) # ------------------ Update D ------------------ logger.log("Optimizing Discriminator...") logger.log(fmt_row(13, discriminator.loss_name)) ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob)) batch_size = len(ob) // d_step d_losses = [ ] # list of tuples, each of which gives the loss for a minibatch for ob_batch, ac_batch in dataset.iterbatches( (ob, ac), include_final_partial_batch=False, batch_size=batch_size): ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob_batch)) # update running mean/std for discriminator if hasattr(discriminator, "obs_rms"): discriminator.obs_rms.update( np.concatenate((ob_batch, ob_expert), 0)) *newlosses, g = discriminator.lossandgrad(ob_batch, ac_batch, ob_expert, ac_expert) d_adam.update(allmean(g), d_stepsize) d_losses.append(newlosses) logger.log(fmt_row(13, np.mean(d_losses, axis=0))) lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"] ) # local values listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples lens, rews, true_rets = map(flatten_lists, zip(*listoflrpairs)) true_rewbuffer.extend(true_rets) lenbuffer.extend(lens) rewbuffer.extend(rews) logger.record_tabular("EpLenMean", np.mean(lenbuffer)) logger.record_tabular("EpRewMean", np.mean(rewbuffer)) logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer)) logger.record_tabular("EpThisIter", len(lens)) episodes_so_far += len(lens) timesteps_so_far += sum(lens) iters_so_far += 1 logger.record_tabular("EpisodesSoFar", episodes_so_far) logger.record_tabular("TimestepsSoFar", timesteps_so_far) logger.record_tabular("TimeElapsed", time.time() - tstart) if rank == 0: logger.dump_tabular() g_loss_stats.add_all_summary(writer, g_losses, iters_so_far) d_loss_stats.add_all_summary(writer, np.mean(d_losses, axis=0), iters_so_far) ep_stats.add_all_summary(writer, [ np.mean(true_rewbuffer), np.mean(rewbuffer), np.mean(lenbuffer) ], iters_so_far)
def neglogp(self, x): return 0.5 * U.sum(tf.square((x - self.mean) / self.std), axis=-1) \ + 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[-1]) \ + U.sum(self.logstd, axis=-1)