diff --git a/evaluate.py b/evaluate.py index c9dc617..cf777f2 100644 --- a/evaluate.py +++ b/evaluate.py @@ -21,11 +21,10 @@ from pufferlib.frameworks import cleanrl import pufferlib.policy_ranker import pufferlib.utils -import clean_pufferl import environment -from reinforcement_learning import config +from reinforcement_learning import config, clean_pufferl def setup_policy_store(policy_store_dir): # CHECK ME: can be custom models with different architectures loaded here? @@ -62,7 +61,7 @@ def save_replays(policy_store_dir, save_dir): from reinforcement_learning import policy # import your policy def make_policy(envs): learner_policy = policy.Baseline( - envs._driver_env, + envs.driver_env, input_size=args.input_size, hidden_size=args.hidden_size, task_size=args.task_size @@ -172,7 +171,7 @@ def rank_policies(policy_store_dir, eval_curriculum_file, device): from reinforcement_learning import policy # import your policy def make_policy(envs): learner_policy = policy.Baseline( - envs, + envs.driver_env, input_size=args.input_size, hidden_size=args.hidden_size, task_size=args.task_size diff --git a/reinforcement_learning/clean_pufferl.py b/reinforcement_learning/clean_pufferl.py deleted file mode 120000 index 621fb3b..0000000 --- a/reinforcement_learning/clean_pufferl.py +++ /dev/null @@ -1 +0,0 @@ -../../pufferlib/clean_pufferl.py \ No newline at end of file diff --git a/reinforcement_learning/clean_pufferl.py b/reinforcement_learning/clean_pufferl.py new file mode 100644 index 0000000..262f907 --- /dev/null +++ b/reinforcement_learning/clean_pufferl.py @@ -0,0 +1,710 @@ +# pylint: disable=all +# PufferLib's customized CleanRL PPO + LSTM implementation +from pdb import set_trace as T + +import os +import random +import time +from collections import defaultdict +from dataclasses import dataclass +from datetime import timedelta +from types import SimpleNamespace + +import numpy as np +import psutil +import torch +import torch.nn as nn +import torch.optim as optim +import wandb +from tqdm import tqdm + +import pufferlib +import pufferlib.emulation +import pufferlib.frameworks.cleanrl +import pufferlib.policy_pool +import pufferlib.policy_ranker +import pufferlib.utils +import pufferlib.vectorization + + +def unroll_nested_dict(d): + if not isinstance(d, dict): + return d + + for k, v in d.items(): + if isinstance(v, dict): + for k2, v2 in unroll_nested_dict(v): + yield f"{k}/{k2}", v2 + else: + yield k, v + + +@dataclass +class CleanPuffeRL: + env_creator: callable = None + env_creator_kwargs: dict = None + agent: nn.Module = None + agent_creator: callable = None + agent_kwargs: dict = None + + exp_name: str = os.path.basename(__file__) + + data_dir: str = 'data' + record_loss: bool = False + checkpoint_interval: int = 1 + seed: int = 1 + torch_deterministic: bool = True + vectorization: ... = pufferlib.vectorization.Serial + device: str = torch.device("cuda") if torch.cuda.is_available() else "cpu" + total_timesteps: int = 10_000_000 + learning_rate: float = 2.5e-4 + num_buffers: int = 1 + num_envs: int = 8 + num_cores: int = psutil.cpu_count(logical=False) + cpu_offload: bool = True + verbose: bool = True + batch_size: int = 2**14 + policy_store: pufferlib.policy_store.PolicyStore = None + policy_ranker: pufferlib.policy_ranker.PolicyRanker = None + + policy_pool: pufferlib.policy_pool.PolicyPool = None + policy_selector: pufferlib.policy_ranker.PolicySelector = None + + # Wandb + wandb_entity: str = None + wandb_project: str = None + wandb_extra_data: dict = None + + # Selfplay + selfplay_learner_weight: float = 1.0 + selfplay_num_policies: int = 1 + + def __post_init__(self, *args, **kwargs): + self.start_time = time.time() + + # If data_dir is provided, load the resume state + resume_state = {} + if self.data_dir is not None: + path = os.path.join(self.data_dir, f"trainer.pt") + if os.path.exists(path): + print(f"Loaded checkpoint from {path}") + resume_state = torch.load(path) + print(f"Resuming from update {resume_state['update']} " + f"with policy {resume_state['policy_checkpoint_name']}") + + self.wandb_run_id = resume_state.get("wandb_run_id", None) + self.learning_rate = resume_state.get("learning_rate", self.learning_rate) + + self.global_step = resume_state.get("global_step", 0) + self.agent_step = resume_state.get("agent_step", 0) + self.update = resume_state.get("update", 0) + + self.total_updates = self.total_timesteps // self.batch_size + self.envs_per_worker = self.num_envs // self.num_cores + assert self.num_cores * self.envs_per_worker == self.num_envs + + # Seed everything + random.seed(self.seed) + np.random.seed(self.seed) + if self.seed is not None: + torch.manual_seed(self.seed) + torch.backends.cudnn.deterministic = self.torch_deterministic + + # Create environments + self.process = psutil.Process() + allocated = self.process.memory_info().rss + self.buffers = [ + self.vectorization( + self.env_creator, + env_kwargs=self.env_creator_kwargs, + num_workers=self.num_cores, + envs_per_worker=self.envs_per_worker, + ) + for _ in range(self.num_buffers) + ] + self.num_agents = self.buffers[0].num_agents + + # If an agent_creator is provided, use envs (=self.buffers[0]) to create the agent + self.agent = pufferlib.emulation.make_object( + self.agent, self.agent_creator, self.buffers[:1], self.agent_kwargs) + + if self.verbose: + print( + "Allocated %.2f MB to environments. Only accurate for Serial backend." + % ((self.process.memory_info().rss - allocated) / 1e6) + ) + + # Create policy store + if self.policy_store is None: + if self.data_dir is not None: + self.policy_store = pufferlib.policy_store.DirectoryPolicyStore( + os.path.join(self.data_dir, "policies") + ) + + # Create policy ranker + if self.policy_ranker is None: + if self.data_dir is not None: + self.policy_ranker = pufferlib.utils.PersistentObject( + os.path.join(self.data_dir, "openskill.pickle"), + pufferlib.policy_ranker.OpenSkillRanker, + "anchor", + ) + if "learner" not in self.policy_ranker.ratings(): + self.policy_ranker.add_policy("learner") + + # Setup agent + if "policy_checkpoint_name" in resume_state: + self.agent = self.policy_store.get_policy( + resume_state["policy_checkpoint_name"] + ).policy(policy_args=[self.buffers[0]]) + + # TODO: this can be cleaned up + self.agent.is_recurrent = hasattr(self.agent, "lstm") + self.agent = self.agent.to(self.device) + + # Setup policy pool + if self.policy_pool is None: + self.policy_pool = pufferlib.policy_pool.PolicyPool( + self.agent, + "learner", + num_envs=self.num_envs, + num_agents=self.num_agents, + learner_weight=self.selfplay_learner_weight, + num_policies=self.selfplay_num_policies, + ) + + # Setup policy selector + if self.policy_selector is None: + self.policy_selector = pufferlib.policy_ranker.PolicySelector( + self.selfplay_num_policies - 1, exclude_names="learner" + ) + + # Setup optimizer + self.optimizer = optim.Adam( + self.agent.parameters(), lr=self.learning_rate, eps=1e-5 + ) + if "optimizer_state_dict" in resume_state: + self.optimizer.load_state_dict(resume_state["optimizer_state_dict"]) + + ### Allocate Storage + next_obs, next_done, next_lstm_state = [], [], [] + for i, envs in enumerate(self.buffers): + envs.async_reset(self.seed + i) + next_done.append( + torch.zeros((self.num_envs * self.num_agents,)).to(self.device) + ) + next_obs.append([]) + + if self.agent.is_recurrent: + shape = ( + self.agent.lstm.num_layers, + self.num_envs * self.num_agents, + self.agent.lstm.hidden_size, + ) + next_lstm_state.append( + ( + torch.zeros(shape).to(self.device), + torch.zeros(shape).to(self.device), + ) + ) + else: + next_lstm_state.append(None) + + allocated_torch = torch.cuda.memory_allocated(self.device) + allocated_cpu = self.process.memory_info().rss + self.data = SimpleNamespace( + buf=0, + sort_keys=[], + next_obs=next_obs, + next_done=next_done, + next_lstm_state=next_lstm_state, + obs=torch.zeros( + self.batch_size + 1, *self.buffers[0].single_observation_space.shape + ).to("cpu" if self.cpu_offload else self.device), + actions=torch.zeros( + self.batch_size + 1, *self.buffers[0].single_action_space.shape, dtype=int + ).to(self.device), + logprobs=torch.zeros(self.batch_size + 1).to(self.device), + rewards=torch.zeros(self.batch_size + 1).to(self.device), + dones=torch.zeros(self.batch_size + 1).to(self.device), + values=torch.zeros(self.batch_size + 1).to(self.device), + ) + + allocated_torch = torch.cuda.memory_allocated(self.device) - allocated_torch + allocated_cpu = self.process.memory_info().rss - allocated_cpu + if self.verbose: + print( + "Allocated to storage - Pytorch: %.2f GB, System: %.2f GB" + % (allocated_torch / 1e9, allocated_cpu / 1e9) + ) + + if self.record_loss and self.data_dir is not None: + self.loss_file = os.path.join(self.data_dir, "loss.txt") + with open(self.loss_file, "w") as f: + pass + self.action_file = os.path.join(self.data_dir, "actions.txt") + with open(self.action_file, "w") as f: + pass + + if self.wandb_entity is not None: + self.wandb_run_id = self.wandb_run_id or wandb.util.generate_id() + + wandb.init( + id=self.wandb_run_id, + project=self.wandb_project, + entity=self.wandb_entity, + config=self.wandb_extra_data or {}, + sync_tensorboard=True, + name=self.exp_name, + monitor_gym=True, + save_code=True, + resume="allow", + ) + + @pufferlib.utils.profile + def evaluate(self, show_progress=False): + # Pick new policies for the policy pool + # TODO: find a way to not switch mid-stream + self.policy_pool.update_policies({ + p.name: p.policy( + policy_args=[self.buffers[0]], + device=self.device, + ) for p in self.policy_store.select_policies(self.policy_selector) + }) + + allocated_torch = torch.cuda.memory_allocated(self.device) + allocated_cpu = self.process.memory_info().rss + ptr = env_step_time = inference_time = agent_steps_collected = 0 + padded_steps_collected = 0 + + step = 0 + infos = defaultdict(lambda: defaultdict(list)) + stats = defaultdict(lambda: defaultdict(list)) + performance = defaultdict(list) + progress_bar = tqdm(total=self.batch_size, disable=not show_progress) + + data = self.data + while True: + buf = data.buf + + step += 1 + if ptr == self.batch_size + 1: + break + + start = time.time() + o, r, d, i = self.buffers[buf].recv() + env_step_time += time.time() - start + + i = self.policy_pool.update_scores(i, "return") + + ''' + for profile in self.buffers[buf].profile(): + for k, v in profile.items(): + performance[k].append(v["delta"]) + ''' + + o = torch.Tensor(o) + if not self.cpu_offload: + o = o.to(self.device) + + r = torch.Tensor(r).float().to(self.device).view(-1) + + if len(d) != 0 and len(data.next_done[buf]) != 0: + alive_mask = (data.next_done[buf].cpu() + torch.Tensor(d)) != 2 + data.next_done[buf] = torch.Tensor(d).to(self.device) + else: + alive_mask = [1 for _ in range(len(o))] + + agent_steps_collected += sum(alive_mask) + padded_steps_collected += len(alive_mask) + + # ALGO LOGIC: action logic + start = time.time() + with torch.no_grad(): + ( + actions, + logprob, + value, + data.next_lstm_state[buf], + ) = self.policy_pool.forwards( + o.to(self.device), + data.next_lstm_state[buf], + data.next_done[buf], + ) + value = value.flatten() + inference_time += time.time() - start + + # TRY NOT TO MODIFY: execute the game + start = time.time() + self.buffers[buf].send(actions.cpu().numpy(), None) + env_step_time += time.time() - start + data.buf = (data.buf + 1) % self.num_buffers + + # Index alive mask with policy pool idxs... + # TODO: Find a way to avoid having to do this + if self.selfplay_learner_weight > 0: + alive_mask = np.array(alive_mask) * self.policy_pool.learner_mask + + for idx in np.where(alive_mask)[0]: + if ptr == self.batch_size + 1: + break + + data.obs[ptr] = o[idx] + data.values[ptr] = value[idx] + data.actions[ptr] = actions[idx] + data.logprobs[ptr] = logprob[idx] + data.sort_keys.append((buf, idx, step)) + + if len(d) != 0: + data.rewards[ptr] = r[idx] + data.dones[ptr] = d[idx] + + ptr += 1 + progress_bar.update(1) + + ''' + for ii in i: + if not ii: + continue + + for agent_i, values in ii.items(): + for name, stat in unroll_nested_dict(values): + infos[name].append(stat) + try: + stat = float(stat) + stats[name].append(stat) + except: + continue + ''' + + for policy_name, policy_i in i.items(): + for agent_i in policy_i: + if not agent_i: + continue + + for name, stat in unroll_nested_dict(agent_i): + infos[policy_name][name].append(stat) + if 'Task_eval_fn' in name: + # Temporary hack for NMMO competition + continue + try: + stat = float(stat) + stats[policy_name][name].append(stat) + except: + continue + + if self.policy_pool.scores and self.policy_ranker is not None: + self.policy_ranker.update_ranks( + self.policy_pool.scores, + wandb_policies=[self.policy_pool._learner_name] + if self.wandb_entity + else [], + step=self.global_step, + ) + self.policy_pool.scores = {} + + env_sps = int(agent_steps_collected / env_step_time) + inference_sps = int(padded_steps_collected / inference_time) + + progress_bar.set_description( + "Eval: " + + ", ".join( + [ + f"Env SPS: {env_sps}", + f"Inference SPS: {inference_sps}", + f"Agent Steps: {agent_steps_collected}", + *[f"{k}: {np.mean(v):.2f}" for k, v in stats['learner'].items()], + ] + ) + ) + + self.global_step += self.batch_size + + if self.wandb_entity: + wandb.log( + { + "performance/env_time": env_step_time, + "performance/env_sps": env_sps, + "performance/inference_time": inference_time, + "performance/inference_sps": inference_sps, + **{ + f"performance/env/{k}": np.mean(v) + for k, v in performance.items() + }, + **{f"charts/{k}": np.mean(v) for k, v in stats['learner'].items()}, + "charts/reward": float(torch.mean(data.rewards)), + "agent_steps": self.global_step, + "global_step": self.global_step, + } + ) + + allocated_torch = torch.cuda.memory_allocated(self.device) - allocated_torch + allocated_cpu = self.process.memory_info().rss - allocated_cpu + if self.verbose: + print( + "Allocated during evaluation - Pytorch: %.2f GB, System: %.2f GB" + % (allocated_torch / 1e9, allocated_cpu / 1e9) + ) + + uptime = timedelta(seconds=int(time.time() - self.start_time)) + print( + f"Epoch: {self.update} - {self.global_step // 1000}K steps - {uptime} Elapsed\n" + f"\tSteps Per Second: Env={env_sps}, Inference={inference_sps}" + ) + + progress_bar.close() + return data, stats, infos + + @pufferlib.utils.profile + def train( + self, + batch_rows=32, + update_epochs=4, + bptt_horizon=16, + gamma=0.99, + gae_lambda=0.95, + anneal_lr=True, + norm_adv=True, + clip_coef=0.1, + clip_vloss=True, + ent_coef=0.01, + vf_coef=0.5, + max_grad_norm=0.5, + target_kl=None, + ): + if self.done_training(): + raise RuntimeError( + f"Trying to train for more than max_updates={self.total_updates} updates" + ) + + # assert self.num_steps % bptt_horizon == 0, "num_steps must be divisible by bptt_horizon" + allocated_torch = torch.cuda.memory_allocated(self.device) + allocated_cpu = self.process.memory_info().rss + + # Annealing the rate if instructed to do so. + if anneal_lr: + frac = 1.0 - (self.update - 1.0) / self.total_updates + lrnow = frac * self.learning_rate + self.optimizer.param_groups[0]["lr"] = lrnow + + # Sort here + data = self.data + idxs = sorted(range(len(data.sort_keys)), key=data.sort_keys.__getitem__) + data.sort_keys = [] + + num_minibatches = self.batch_size // bptt_horizon // batch_rows + b_idxs = ( + torch.Tensor(idxs) + .long()[:-1] + .reshape(batch_rows, num_minibatches, bptt_horizon) + .transpose(0, 1) + ) + + # bootstrap value if not done + with torch.no_grad(): + advantages = torch.zeros(self.batch_size, device=self.device) + lastgaelam = 0 + for t in reversed(range(self.batch_size)): + i, i_nxt = idxs[t], idxs[t + 1] + nextnonterminal = 1.0 - data.dones[i_nxt] + nextvalues = data.values[i_nxt] + delta = ( + data.rewards[i_nxt] + + gamma * nextvalues * nextnonterminal + - data.values[i] + ) + advantages[t] = lastgaelam = ( + delta + gamma * gae_lambda * nextnonterminal * lastgaelam + ) + + # Flatten the batch + data.b_obs = b_obs = data.obs[b_idxs] + b_actions = data.actions[b_idxs] + b_logprobs = data.logprobs[b_idxs] + b_dones = data.dones[b_idxs] + b_values = data.values[b_idxs] + b_advantages = advantages.reshape( + batch_rows, num_minibatches, bptt_horizon + ).transpose(0, 1) + b_returns = b_advantages + b_values + + # Optimizing the policy and value network + train_time = time.time() + clipfracs = [] + for epoch in range(update_epochs): + lstm_state = None + for mb in range(num_minibatches): + mb_obs = b_obs[mb].to(self.device) + mb_actions = b_actions[mb].contiguous() + mb_values = b_values[mb].reshape(-1) + mb_advantages = b_advantages[mb].reshape(-1) + mb_returns = b_returns[mb].reshape(-1) + + if self.agent.is_recurrent: + ( + _, + newlogprob, + entropy, + newvalue, + lstm_state, + ) = self.agent.get_action_and_value( + mb_obs, state=lstm_state, done=b_dones[mb], action=mb_actions + ) + lstm_state = (lstm_state[0].detach(), lstm_state[1].detach()) + else: + _, newlogprob, entropy, newvalue = self.agent.get_action_and_value( + mb_obs.reshape( + -1, *self.buffers[0].single_observation_space.shape + ), + action=mb_actions, + ) + + logratio = newlogprob - b_logprobs[mb].reshape(-1) + ratio = logratio.exp() + + with torch.no_grad(): + # calculate approx_kl http://joschu.net/blog/kl-approx.html + old_approx_kl = (-logratio).mean() + approx_kl = ((ratio - 1) - logratio).mean() + clipfracs += [ + ((ratio - 1.0).abs() > clip_coef).float().mean().item() + ] + + mb_advantages = mb_advantages.reshape(-1) + if norm_adv: + mb_advantages = (mb_advantages - mb_advantages.mean()) / ( + mb_advantages.std() + 1e-8 + ) + + # Policy loss + pg_loss1 = -mb_advantages * ratio + pg_loss2 = -mb_advantages * torch.clamp( + ratio, 1 - clip_coef, 1 + clip_coef + ) + pg_loss = torch.max(pg_loss1, pg_loss2).mean() + + # Value loss + newvalue = newvalue.view(-1) + if clip_vloss: + v_loss_unclipped = (newvalue - mb_returns) ** 2 + v_clipped = mb_values + torch.clamp( + newvalue - mb_values, + -clip_coef, + clip_coef, + ) + v_loss_clipped = (v_clipped - mb_returns) ** 2 + v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) + v_loss = 0.5 * v_loss_max.mean() + else: + v_loss = 0.5 * ((newvalue - mb_returns) ** 2).mean() + + entropy_loss = entropy.mean() + loss = pg_loss - ent_coef * entropy_loss + v_loss * vf_coef + + if self.record_loss: + with open(self.loss_file, "a") as f: + print(f"# mini batch ({epoch}, {mb}) -- pg_loss:{pg_loss.item():.4f}, value_loss:{v_loss.item():.4f}, " + \ + f"entropy:{entropy_loss.item():.4f}, approx_kl: {approx_kl.item():.4f}", + file=f) + with open(self.action_file, "a") as f: + print(f"# mini batch ({epoch}, {mb}) -- pg_loss:{pg_loss.item():.4f}, value_loss:{v_loss.item():.4f}, " + \ + f"entropy:{entropy_loss.item():.4f}, approx_kl: {approx_kl.item():.4f}", + file=f) + atn_list = mb_actions.cpu().numpy().tolist() + for atns in atn_list: + for atn in atns: + print(f"{atn}", file=f) + + self.optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(self.agent.parameters(), max_grad_norm) + self.optimizer.step() + + if target_kl is not None: + if approx_kl > target_kl: + break + + y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() + var_y = np.var(y_true) + explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y + + # TIMING: performance metrics to evaluate cpu/gpu usage + train_time = time.time() - train_time + train_sps = int(self.batch_size / train_time) + self.update += 1 + + print(f"\tTrain={train_sps}\n") + + allocated_torch = torch.cuda.memory_allocated(self.device) - allocated_torch + allocated_cpu = self.process.memory_info().rss - allocated_cpu + if self.verbose: + print( + "Allocated during training - Pytorch: %.2f GB, System: %.2f GB" + % (allocated_torch / 1e9, allocated_cpu / 1e9) + ) + + if self.record_loss: + with open(self.loss_file, "a") as f: + print(f"Epoch -- policy_loss:{pg_loss.item():.4f}, value_loss:{v_loss.item():.4f}, ", + f"entropy:{entropy_loss.item():.4f}, approx_kl:{approx_kl.item():.4f}", + f"clipfrac:{np.mean(clipfracs):.4f}, explained_var:{explained_var:.4f}", + file=f) + + # TRY NOT TO MODIFY: record rewards for plotting purposes + if self.wandb_entity: + wandb.log( + { + "performance/train_sps": train_sps, + "performance/train_time": train_time, + "charts/learning_rate": self.optimizer.param_groups[0]["lr"], + "losses/value_loss": v_loss.item(), + "losses/policy_loss": pg_loss.item(), + "losses/entropy": entropy_loss.item(), + "losses/old_approx_kl": old_approx_kl.item(), + "losses/approx_kl": approx_kl.item(), + "losses/clipfrac": np.mean(clipfracs), + "losses/explained_variance": explained_var, + "agent_steps": self.global_step, + "global_step": self.global_step, + } + ) + + if self.update % self.checkpoint_interval == 1 or self.done_training(): + self._save_checkpoint() + + def done_training(self): + return self.update >= self.total_updates + + def close(self): + for envs in self.buffers: + envs.close() + + if self.wandb_entity: + wandb.finish() + + def _save_checkpoint(self): + if self.data_dir is None: + return + + policy_name = f"{self.exp_name}.{self.update:06d}" + state = { + "optimizer_state_dict": self.optimizer.state_dict(), + "global_step": self.global_step, + "agent_step": self.agent_step, + "update": self.update, + "learning_rate": self.learning_rate, + "policy_checkpoint_name": policy_name, + "wandb_run_id": self.wandb_run_id, + } + path = os.path.join(self.data_dir, f"trainer.pt") + tmp_path = path + ".tmp" + torch.save(state, tmp_path) + os.rename(tmp_path, path) + + # NOTE: as the agent_creator has args internally, the policy args are not passed + self.policy_store.add_policy(policy_name, self.agent) + + if self.policy_ranker: + self.policy_ranker.add_policy_copy( + policy_name, self.policy_pool._learner_name + ) diff --git a/reinforcement_learning/policy.py b/reinforcement_learning/policy.py index 3bb4d58..39afde5 100644 --- a/reinforcement_learning/policy.py +++ b/reinforcement_learning/policy.py @@ -36,7 +36,7 @@ def critic(self, hidden): class Baseline(pufferlib.models.Policy): def __init__(self, env, input_size=256, hidden_size=256, task_size=4096): - super().__init__() + super().__init__(env) self.flat_observation_space = env.flat_observation_space self.flat_observation_structure = env.flat_observation_structure diff --git a/train.py b/train.py index 08f08c0..fa6da7e 100644 --- a/train.py +++ b/train.py @@ -5,12 +5,10 @@ from pufferlib.vectorization import Serial, Multiprocessing from pufferlib.policy_store import DirectoryPolicyStore from pufferlib.frameworks import cleanrl -import clean_pufferl import environment -from reinforcement_learning import policy -from reinforcement_learning import config +from reinforcement_learning import clean_pufferl, policy, config # NOTE: this file changes when running curriculum generation track # Run test_task_encoder.py to regenerate this file (or get it from the repo) @@ -31,7 +29,7 @@ def setup_env(args): def make_policy(envs): learner_policy = policy.Baseline( - envs._driver_env, + envs.driver_env, input_size=args.input_size, hidden_size=args.hidden_size, task_size=args.task_size