406 lines
18 KiB
Python
406 lines
18 KiB
Python
"""
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Use diffusion exact likelihood for policy gradient.
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"""
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import os
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import pickle
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import einops
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import numpy as np
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import torch
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import logging
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import wandb
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log = logging.getLogger(__name__)
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from util.timer import Timer
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from agent.finetune.train_ppo_diffusion_agent import TrainPPODiffusionAgent
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class TrainPPOExactDiffusionAgent(TrainPPODiffusionAgent):
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def __init__(self, cfg):
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super().__init__(cfg)
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def run(self):
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"""
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For exact likelihood, we do not need to save the chains.
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"""
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# Start training loop
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timer = Timer()
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run_results = []
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last_itr_eval = False
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done_venv = np.zeros((1, self.n_envs))
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while self.itr < self.n_train_itr:
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# Prepare video paths for each envs --- only applies for the first set of episodes if allowing reset within iteration and each iteration has multiple episodes from one env
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options_venv = [{} for _ in range(self.n_envs)]
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if self.itr % self.render_freq == 0 and self.render_video:
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for env_ind in range(self.n_render):
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options_venv[env_ind]["video_path"] = os.path.join(
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self.render_dir, f"itr-{self.itr}_trial-{env_ind}.mp4"
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)
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# Define train or eval - all envs restart
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eval_mode = self.itr % self.val_freq == 0 and not self.force_train
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self.model.eval() if eval_mode else self.model.train()
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last_itr_eval = eval_mode
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# Reset env before iteration starts (1) if specified, (2) at eval mode, or (3) right after eval mode
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dones_trajs = np.empty((0, self.n_envs))
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firsts_trajs = np.zeros((self.n_steps + 1, self.n_envs))
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if self.reset_at_iteration or eval_mode or last_itr_eval:
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prev_obs_venv = self.reset_env_all(options_venv=options_venv)
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firsts_trajs[0] = 1
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else:
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firsts_trajs[0] = (
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done_venv # if done at the end of last iteration, then the envs are just reset
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)
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# Holder
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obs_trajs = np.empty((0, self.n_envs, self.n_cond_step, self.obs_dim))
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samples_trajs = np.empty(
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(
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0,
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self.n_envs,
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self.horizon_steps,
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self.action_dim,
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)
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)
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chains_trajs = np.empty(
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(
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0,
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self.n_envs,
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self.model.ft_denoising_steps + 1,
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self.horizon_steps,
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self.action_dim,
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)
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)
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reward_trajs = np.empty((0, self.n_envs))
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obs_full_trajs = np.empty((0, self.n_envs, self.obs_dim))
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obs_full_trajs = np.vstack(
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(obs_full_trajs, prev_obs_venv[None].squeeze(2))
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) # remove cond_step dim
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# Collect a set of trajectories from env
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for step in range(self.n_steps):
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if step % 10 == 0:
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print(f"Processed step {step} of {self.n_steps}")
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# Select action
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with torch.no_grad():
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samples = self.model(
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cond=torch.from_numpy(prev_obs_venv).float().to(self.device),
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deterministic=eval_mode,
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return_chain=True,
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)
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output_venv = (
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samples.trajectories.cpu().numpy()
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) # n_env x horizon x act
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chains_venv = (
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samples.chains.cpu().numpy()
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) # n_env x denoising x horizon x act
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action_venv = output_venv[:, : self.act_steps]
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# Apply multi-step action
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obs_venv, reward_venv, done_venv, info_venv = self.venv.step(
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action_venv
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)
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if self.save_full_observations:
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obs_full_venv = np.vstack(
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[info["full_obs"][None] for info in info_venv]
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) # n_envs x n_act_steps x obs_dim
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obs_full_trajs = np.vstack(
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(obs_full_trajs, obs_full_venv.transpose(1, 0, 2))
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)
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obs_trajs = np.vstack((obs_trajs, prev_obs_venv[None]))
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chains_trajs = np.vstack((chains_trajs, chains_venv[None]))
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samples_trajs = np.vstack((samples_trajs, output_venv[None]))
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reward_trajs = np.vstack((reward_trajs, reward_venv[None]))
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dones_trajs = np.vstack((dones_trajs, done_venv[None]))
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firsts_trajs[step + 1] = done_venv
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prev_obs_venv = obs_venv
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# Summarize episode reward --- this needs to be handled differently depending on whether the environment is reset after each iteration. Only count episodes that finish within the iteration.
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episodes_start_end = []
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for env_ind in range(self.n_envs):
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env_steps = np.where(firsts_trajs[:, env_ind] == 1)[0]
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for i in range(len(env_steps) - 1):
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start = env_steps[i]
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end = env_steps[i + 1]
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if end - start > 1:
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episodes_start_end.append((env_ind, start, end - 1))
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if len(episodes_start_end) > 0:
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reward_trajs_split = [
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reward_trajs[start : end + 1, env_ind]
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for env_ind, start, end in episodes_start_end
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]
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num_episode_finished = len(reward_trajs_split)
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episode_reward = np.array(
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[np.sum(reward_traj) for reward_traj in reward_trajs_split]
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)
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episode_best_reward = np.array(
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[
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np.max(reward_traj) / self.act_steps
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for reward_traj in reward_trajs_split
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]
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)
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avg_episode_reward = np.mean(episode_reward)
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avg_best_reward = np.mean(episode_best_reward)
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success_rate = np.mean(
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episode_best_reward >= self.best_reward_threshold_for_success
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)
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else:
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episode_reward = np.array([])
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num_episode_finished = 0
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avg_episode_reward = 0
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avg_best_reward = 0
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success_rate = 0
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log.info("[WARNING] No episode completed within the iteration!")
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# Update
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if not eval_mode:
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with torch.no_grad():
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# Calculate value and logprobs - split into batches to prevent out of memory
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obs_t = einops.rearrange(
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torch.from_numpy(obs_trajs).float().to(self.device),
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"s e h d -> (s e) h d",
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)
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obs_ts = torch.split(obs_t, self.logprob_batch_size, dim=0)
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values_trajs = np.empty((0, self.n_envs))
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for obs in obs_ts:
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values = self.model.critic(obs).cpu().numpy().flatten()
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values_trajs = np.vstack(
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(values_trajs, values.reshape(-1, self.n_envs))
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)
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samples_t = einops.rearrange(
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torch.from_numpy(samples_trajs).float().to(self.device),
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"s e h d -> (s e) h d",
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)
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samples_ts = torch.split(samples_t, self.logprob_batch_size, dim=0)
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logprobs_trajs = np.empty((0))
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for obs, samples in zip(obs_ts, samples_ts):
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logprobs = (
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self.model.get_exact_logprobs(obs, samples).cpu().numpy()
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)
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logprobs_trajs = np.concatenate((logprobs_trajs, logprobs))
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# normalize reward with running variance if specified
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if self.reward_scale_running:
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reward_trajs_transpose = self.running_reward_scaler(
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reward=reward_trajs.T, first=firsts_trajs[:-1].T
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)
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reward_trajs = reward_trajs_transpose.T
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# bootstrap value with GAE if not done - apply reward scaling with constant if specified
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obs_venv_ts = torch.from_numpy(obs_venv).float().to(self.device)
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with torch.no_grad():
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next_value = (
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self.model.critic(obs_venv_ts).reshape(1, -1).cpu().numpy()
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)
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advantages_trajs = np.zeros_like(reward_trajs)
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lastgaelam = 0
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for t in reversed(range(self.n_steps)):
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if t == self.n_steps - 1:
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nextnonterminal = 1.0 - done_venv
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nextvalues = next_value
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else:
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nextnonterminal = 1.0 - dones_trajs[t + 1]
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nextvalues = values_trajs[t + 1]
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# delta = r + gamma*V(st+1) - V(st)
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delta = (
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reward_trajs[t] * self.reward_scale_const
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+ self.gamma * nextvalues * nextnonterminal
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- values_trajs[t]
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)
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# A = delta_t + gamma*lamdba*delta_{t+1} + ...
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advantages_trajs[t] = lastgaelam = (
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delta
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+ self.gamma
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* self.gae_lambda
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* nextnonterminal
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* lastgaelam
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)
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returns_trajs = advantages_trajs + values_trajs
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# k for environment step
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obs_k = einops.rearrange(
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torch.tensor(obs_trajs).float().to(self.device),
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"s e h d -> (s e) h d",
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)
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samples_k = einops.rearrange(
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torch.tensor(samples_trajs).float().to(self.device),
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"s e h d -> (s e) h d",
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)
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returns_k = (
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torch.tensor(returns_trajs).float().to(self.device).reshape(-1)
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)
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values_k = (
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torch.tensor(values_trajs).float().to(self.device).reshape(-1)
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)
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advantages_k = (
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torch.tensor(advantages_trajs).float().to(self.device).reshape(-1)
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)
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logprobs_k = torch.tensor(logprobs_trajs).float().to(self.device)
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# Update policy and critic
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total_steps = self.n_steps * self.n_envs
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inds_k = np.arange(total_steps)
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clipfracs = []
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for update_epoch in range(self.update_epochs):
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# for each epoch, go through all data in batches
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flag_break = False
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np.random.shuffle(inds_k)
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num_batch = max(1, total_steps // self.batch_size) # skip last ones
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for batch in range(num_batch):
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start = batch * self.batch_size
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end = start + self.batch_size
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inds_b = inds_k[start:end] # b for batch
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obs_b = obs_k[inds_b]
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samples_b = samples_k[inds_b]
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returns_b = returns_k[inds_b]
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values_b = values_k[inds_b]
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advantages_b = advantages_k[inds_b]
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logprobs_b = logprobs_k[inds_b]
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# get loss
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(
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pg_loss,
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v_loss,
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clipfrac,
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approx_kl,
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ratio,
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bc_loss,
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) = self.model.loss(
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obs_b,
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samples_b,
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returns_b,
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values_b,
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advantages_b,
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logprobs_b,
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use_bc_loss=self.use_bc_loss,
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reward_horizon=self.reward_horizon,
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)
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loss = (
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pg_loss
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+ v_loss * self.vf_coef
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+ bc_loss * self.bc_loss_coeff
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)
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clipfracs += [clipfrac]
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# update policy and critic
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self.actor_optimizer.zero_grad()
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self.critic_optimizer.zero_grad()
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loss.backward()
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if self.itr >= self.n_critic_warmup_itr:
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if self.max_grad_norm is not None:
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torch.nn.utils.clip_grad_norm_(
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self.model.actor_ft.parameters(), self.max_grad_norm
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)
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self.actor_optimizer.step()
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self.critic_optimizer.step()
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log.info(
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f"approx_kl: {approx_kl}, update_epoch: {update_epoch}, num_batch: {num_batch}"
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)
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# Stop gradient update if KL difference reaches target
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if self.target_kl is not None and approx_kl > self.target_kl:
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flag_break = True
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break
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if flag_break:
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break
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# Explained variation of future rewards using value function
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y_pred, y_true = values_k.cpu().numpy(), returns_k.cpu().numpy()
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var_y = np.var(y_true)
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explained_var = (
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np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
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)
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# Plot state trajectories
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if (
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self.itr % self.render_freq == 0
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and self.n_render > 0
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and self.traj_plotter is not None
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):
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self.traj_plotter(
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obs_full_trajs=obs_full_trajs,
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n_render=self.n_render,
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max_episode_steps=self.max_episode_steps,
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render_dir=self.render_dir,
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itr=self.itr,
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)
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# Update lr
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if self.itr >= self.n_critic_warmup_itr:
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self.actor_lr_scheduler.step()
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self.critic_lr_scheduler.step()
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# Save model
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if self.itr % self.save_model_freq == 0 or self.itr == self.n_train_itr - 1:
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self.save_model()
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# Log loss and save metrics
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run_results.append(
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{
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"itr": self.itr,
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}
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)
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if self.save_trajs:
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run_results[-1]["obs_full_trajs"] = obs_full_trajs
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run_results[-1]["obs_trajs"] = obs_trajs
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run_results[-1]["action_trajs"] = samples_trajs
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run_results[-1]["chains_trajs"] = chains_trajs
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run_results[-1]["reward_trajs"] = reward_trajs
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if self.itr % self.log_freq == 0:
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if eval_mode:
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log.info(
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f"eval: success rate {success_rate:8.4f} | avg episode reward {avg_episode_reward:8.4f} | avg best reward {avg_best_reward:8.4f}"
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)
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if self.use_wandb:
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wandb.log(
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{
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"success rate - eval": success_rate,
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"avg episode reward - eval": avg_episode_reward,
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"avg best reward - eval": avg_best_reward,
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"num episode - eval": num_episode_finished,
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},
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step=self.itr,
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commit=False,
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)
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run_results[-1]["eval_success_rate"] = success_rate
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run_results[-1]["eval_episode_reward"] = avg_episode_reward
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run_results[-1]["eval_best_reward"] = avg_best_reward
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else:
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log.info(
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f"{self.itr}: loss {loss:8.4f} | pg loss {pg_loss:8.4f} | value loss {v_loss:8.4f} | reward {avg_episode_reward:8.4f} | t:{timer():8.4f}"
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)
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if self.use_wandb:
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wandb.log(
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{
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"loss": loss,
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"pg loss": pg_loss,
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"value loss": v_loss,
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"approx kl": approx_kl,
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"ratio": ratio,
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"clipfrac": np.mean(clipfracs),
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"explained variance": explained_var,
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"avg episode reward - train": avg_episode_reward,
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},
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step=self.itr,
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commit=True,
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)
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run_results[-1]["loss"] = loss
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run_results[-1]["pg_loss"] = pg_loss
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run_results[-1]["value_loss"] = v_loss
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run_results[-1]["approx_kl"] = approx_kl
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run_results[-1]["ratio"] = ratio
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run_results[-1]["clip_frac"] = np.mean(clipfracs)
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run_results[-1]["explained_variance"] = explained_var
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run_results[-1]["train_episode_reward"] = avg_episode_reward
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run_results[-1]["time"] = timer()
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with open(self.result_path, "wb") as f:
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pickle.dump(run_results, f)
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self.itr += 1
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