import logging import ray from ray.rllib.evaluation.postprocessing import compute_gae_for_sample_batch, \ Postprocessing from ray.rllib.policy.policy_template import build_policy_class from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.agents.ppo.ppo_tf_policy import setup_config from ray.rllib.agents.ppo.ppo_torch_policy import vf_preds_fetches, \ ValueNetworkMixin from ray.rllib.utils.torch_ops import apply_grad_clipping from ray.rllib.utils.framework import try_import_torch torch, nn = try_import_torch() logger = logging.getLogger(__name__) def PPOLoss(dist_class, actions, curr_logits, behaviour_logits, advantages, value_fn, value_targets, vf_preds, cur_kl_coeff, entropy_coeff, clip_param, vf_clip_param, vf_loss_coeff, clip_loss=False): def surrogate_loss(actions, curr_dist, prev_dist, advantages, clip_param, clip_loss): pi_new_logp = curr_dist.logp(actions) pi_old_logp = prev_dist.logp(actions) logp_ratio = torch.exp(pi_new_logp - pi_old_logp) if clip_loss: return torch.min( advantages * logp_ratio, advantages * torch.clamp(logp_ratio, 1 - clip_param, 1 + clip_param)) return advantages * logp_ratio def kl_loss(curr_dist, prev_dist): return prev_dist.kl(curr_dist) def entropy_loss(dist): return dist.entropy() def vf_loss(value_fn, value_targets, vf_preds, vf_clip_param=0.1): # GAE Value Function Loss vf_loss1 = torch.pow(value_fn - value_targets, 2.0) vf_clipped = vf_preds + torch.clamp(value_fn - vf_preds, -vf_clip_param, vf_clip_param) vf_loss2 = torch.pow(vf_clipped - value_targets, 2.0) vf_loss = torch.max(vf_loss1, vf_loss2) return vf_loss pi_new_dist = dist_class(curr_logits, None) pi_old_dist = dist_class(behaviour_logits, None) surr_loss = torch.mean( surrogate_loss(actions, pi_new_dist, pi_old_dist, advantages, clip_param, clip_loss)) kl_loss = torch.mean(kl_loss(pi_new_dist, pi_old_dist)) vf_loss = torch.mean( vf_loss(value_fn, value_targets, vf_preds, vf_clip_param)) entropy_loss = torch.mean(entropy_loss(pi_new_dist)) total_loss = -surr_loss + cur_kl_coeff * kl_loss total_loss += vf_loss_coeff * vf_loss total_loss -= entropy_coeff * entropy_loss return total_loss, surr_loss, kl_loss, vf_loss, entropy_loss # This is the computation graph for workers (inner adaptation steps) class WorkerLoss(object): def __init__(self, model, dist_class, actions, curr_logits, behaviour_logits, advantages, value_fn, value_targets, vf_preds, cur_kl_coeff, entropy_coeff, clip_param, vf_clip_param, vf_loss_coeff, clip_loss=False): self.loss, surr_loss, kl_loss, vf_loss, ent_loss = PPOLoss( dist_class=dist_class, actions=actions, curr_logits=curr_logits, behaviour_logits=behaviour_logits, advantages=advantages, value_fn=value_fn, value_targets=value_targets, vf_preds=vf_preds, cur_kl_coeff=cur_kl_coeff, entropy_coeff=entropy_coeff, clip_param=clip_param, vf_clip_param=vf_clip_param, vf_loss_coeff=vf_loss_coeff, clip_loss=clip_loss) # This is the Meta-Update computation graph for main (meta-update step) class MAMLLoss(object): def __init__(self, model, config, dist_class, value_targets, advantages, actions, behaviour_logits, vf_preds, cur_kl_coeff, policy_vars, obs, num_tasks, split, meta_opt, inner_adaptation_steps=1, entropy_coeff=0, clip_param=0.3, vf_clip_param=0.1, vf_loss_coeff=1.0, use_gae=True): import higher self.config = config self.num_tasks = num_tasks self.inner_adaptation_steps = inner_adaptation_steps self.clip_param = clip_param self.dist_class = dist_class self.cur_kl_coeff = cur_kl_coeff self.model = model self.vf_clip_param = vf_clip_param self.vf_loss_coeff = vf_loss_coeff self.entropy_coeff = entropy_coeff # Split episode tensors into [inner_adaptation_steps+1, num_tasks, -1] self.obs = self.split_placeholders(obs, split) self.actions = self.split_placeholders(actions, split) self.behaviour_logits = self.split_placeholders( behaviour_logits, split) self.advantages = self.split_placeholders(advantages, split) self.value_targets = self.split_placeholders(value_targets, split) self.vf_preds = self.split_placeholders(vf_preds, split) inner_opt = torch.optim.SGD(model.parameters(), lr=config["inner_lr"]) surr_losses = [] val_losses = [] kl_losses = [] entropy_losses = [] meta_losses = [] kls = [] meta_opt.zero_grad() for i in range(self.num_tasks): with higher.innerloop_ctx( model, inner_opt, copy_initial_weights=False) as (fnet, diffopt): inner_kls = [] for step in range(self.inner_adaptation_steps): ppo_loss, _, inner_kl_loss, _, _ = self.compute_losses( fnet, step, i) diffopt.step(ppo_loss) inner_kls.append(inner_kl_loss) kls.append(inner_kl_loss.detach()) # Meta Update ppo_loss, s_loss, kl_loss, v_loss, ent = self.compute_losses( fnet, self.inner_adaptation_steps - 1, i, clip_loss=True) inner_loss = torch.mean( torch.stack([ a * b for a, b in zip( self.cur_kl_coeff[ i * self.inner_adaptation_steps:(i + 1) * self.inner_adaptation_steps], inner_kls) ])) meta_loss = (ppo_loss + inner_loss) / self.num_tasks meta_loss.backward() surr_losses.append(s_loss.detach()) kl_losses.append(kl_loss.detach()) val_losses.append(v_loss.detach()) entropy_losses.append(ent.detach()) meta_losses.append(meta_loss.detach()) meta_opt.step() # Stats Logging self.mean_policy_loss = torch.mean(torch.stack(surr_losses)) self.mean_kl_loss = torch.mean(torch.stack(kl_losses)) self.mean_vf_loss = torch.mean(torch.stack(val_losses)) self.mean_entropy = torch.mean(torch.stack(entropy_losses)) self.mean_inner_kl = kls self.loss = torch.sum(torch.stack(meta_losses)) # Hacky, needed to bypass RLlib backend self.loss.requires_grad = True def compute_losses(self, model, inner_adapt_iter, task_iter, clip_loss=False): obs = self.obs[inner_adapt_iter][task_iter] obs_dict = {"obs": obs, "obs_flat": obs} curr_logits, _ = model.forward(obs_dict, None, None) value_fns = model.value_function() ppo_loss, surr_loss, kl_loss, val_loss, ent_loss = PPOLoss( dist_class=self.dist_class, actions=self.actions[inner_adapt_iter][task_iter], curr_logits=curr_logits, behaviour_logits=self.behaviour_logits[inner_adapt_iter][ task_iter], advantages=self.advantages[inner_adapt_iter][task_iter], value_fn=value_fns, value_targets=self.value_targets[inner_adapt_iter][task_iter], vf_preds=self.vf_preds[inner_adapt_iter][task_iter], cur_kl_coeff=0.0, entropy_coeff=self.entropy_coeff, clip_param=self.clip_param, vf_clip_param=self.vf_clip_param, vf_loss_coeff=self.vf_loss_coeff, clip_loss=clip_loss) return ppo_loss, surr_loss, kl_loss, val_loss, ent_loss def split_placeholders(self, placeholder, split): inner_placeholder_list = torch.split( placeholder, torch.sum(split, dim=1).tolist(), dim=0) placeholder_list = [] for index, split_placeholder in enumerate(inner_placeholder_list): placeholder_list.append( torch.split(split_placeholder, split[index].tolist(), dim=0)) return placeholder_list def maml_loss(policy, model, dist_class, train_batch): logits, state = model.from_batch(train_batch) policy.cur_lr = policy.config["lr"] if policy.config["worker_index"]: policy.loss_obj = WorkerLoss( model=model, dist_class=dist_class, actions=train_batch[SampleBatch.ACTIONS], curr_logits=logits, behaviour_logits=train_batch[SampleBatch.ACTION_DIST_INPUTS], advantages=train_batch[Postprocessing.ADVANTAGES], value_fn=model.value_function(), value_targets=train_batch[Postprocessing.VALUE_TARGETS], vf_preds=train_batch[SampleBatch.VF_PREDS], cur_kl_coeff=0.0, entropy_coeff=policy.config["entropy_coeff"], clip_param=policy.config["clip_param"], vf_clip_param=policy.config["vf_clip_param"], vf_loss_coeff=policy.config["vf_loss_coeff"], clip_loss=False) else: policy.var_list = model.named_parameters() # `split` may not exist yet (during test-loss call), use a dummy value. # Cannot use get here due to train_batch being a TrackingDict. if "split" in train_batch: split = train_batch["split"] else: split_shape = (policy.config["inner_adaptation_steps"], policy.config["num_workers"]) split_const = int(train_batch["obs"].shape[0] // (split_shape[0] * split_shape[1])) split = torch.ones(split_shape, dtype=int) * split_const policy.loss_obj = MAMLLoss( model=model, dist_class=dist_class, value_targets=train_batch[Postprocessing.VALUE_TARGETS], advantages=train_batch[Postprocessing.ADVANTAGES], actions=train_batch[SampleBatch.ACTIONS], behaviour_logits=train_batch[SampleBatch.ACTION_DIST_INPUTS], vf_preds=train_batch[SampleBatch.VF_PREDS], cur_kl_coeff=policy.kl_coeff_val, policy_vars=policy.var_list, obs=train_batch[SampleBatch.CUR_OBS], num_tasks=policy.config["num_workers"], split=split, config=policy.config, inner_adaptation_steps=policy.config["inner_adaptation_steps"], entropy_coeff=policy.config["entropy_coeff"], clip_param=policy.config["clip_param"], vf_clip_param=policy.config["vf_clip_param"], vf_loss_coeff=policy.config["vf_loss_coeff"], use_gae=policy.config["use_gae"], meta_opt=policy.meta_opt) return policy.loss_obj.loss def maml_stats(policy, train_batch): if policy.config["worker_index"]: return {"worker_loss": policy.loss_obj.loss} else: return { "cur_kl_coeff": policy.kl_coeff_val, "cur_lr": policy.cur_lr, "total_loss": policy.loss_obj.loss, "policy_loss": policy.loss_obj.mean_policy_loss, "vf_loss": policy.loss_obj.mean_vf_loss, "kl_loss": policy.loss_obj.mean_kl_loss, "inner_kl": policy.loss_obj.mean_inner_kl, "entropy": policy.loss_obj.mean_entropy, } class KLCoeffMixin: def __init__(self, config): self.kl_coeff_val = [ config["kl_coeff"] ] * config["inner_adaptation_steps"] * config["num_workers"] self.kl_target = self.config["kl_target"] def update_kls(self, sampled_kls): for i, kl in enumerate(sampled_kls): if kl < self.kl_target / 1.5: self.kl_coeff_val[i] *= 0.5 elif kl > 1.5 * self.kl_target: self.kl_coeff_val[i] *= 2.0 return self.kl_coeff_val def maml_optimizer_fn(policy, config): """ Workers use simple SGD for inner adaptation Meta-Policy uses Adam optimizer for meta-update """ if not config["worker_index"]: policy.meta_opt = torch.optim.Adam( policy.model.parameters(), lr=config["lr"]) return policy.meta_opt return torch.optim.SGD(policy.model.parameters(), lr=config["inner_lr"]) def setup_mixins(policy, obs_space, action_space, config): ValueNetworkMixin.__init__(policy, obs_space, action_space, config) KLCoeffMixin.__init__(policy, config) MAMLTorchPolicy = build_policy_class( name="MAMLTorchPolicy", framework="torch", get_default_config=lambda: ray.rllib.agents.maml.maml.DEFAULT_CONFIG, loss_fn=maml_loss, stats_fn=maml_stats, optimizer_fn=maml_optimizer_fn, extra_action_out_fn=vf_preds_fetches, postprocess_fn=compute_gae_for_sample_batch, extra_grad_process_fn=apply_grad_clipping, before_init=setup_config, after_init=setup_mixins, mixins=[KLCoeffMixin])