e0842e71dc
* v0.5 (#9) * update idql configs * update awr configs * update dipo configs * update qsm configs * update dqm configs * update project version to 0.5.0
395 lines
16 KiB
Python
395 lines
16 KiB
Python
"""
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Advantage-weighted regression (AWR) for diffusion policy.
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Advantage = discounted-reward-to-go - V(s)
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Do not support pixel input right now.
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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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from copy import deepcopy
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log = logging.getLogger(__name__)
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from util.timer import Timer
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from collections import deque
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from agent.finetune.train_agent import TrainAgent
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from util.scheduler import CosineAnnealingWarmupRestarts
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def td_values(
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states,
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rewards,
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terminateds,
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state_values,
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gamma=0.99,
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alpha=0.95,
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lam=0.95,
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):
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"""
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Gives a list of TD estimates for a given list of samples from an RL environment.
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The TD(λ) estimator is used for this computation.
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:param replay_buffers: The replay buffers filled by exploring the RL environment.
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Includes: states, rewards, "final state?"s.
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:param state_values: The currently estimated state values.
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:return: The TD estimates.
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"""
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sample_count = len(states)
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tds = np.zeros_like(state_values, dtype=np.float32)
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next_value = state_values[-1].copy()
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next_value[terminateds[-1]] = 0.0
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val = 0.0
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for i in range(sample_count - 1, -1, -1):
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# get next_value for vectorized
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if i < sample_count - 1:
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next_value = state_values[i + 1]
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next_value = next_value * (1 - terminateds[i])
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state_value = state_values[i]
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error = rewards[i] + gamma * next_value - state_value
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val = alpha * error + gamma * lam * (1 - terminateds[i]) * val
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tds[i] = val + state_value
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return tds
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class TrainAWRDiffusionAgent(TrainAgent):
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def __init__(self, cfg):
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super().__init__(cfg)
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self.logprob_batch_size = cfg.train.get("logprob_batch_size", 10000)
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# note the discount factor gamma here is applied to reward every act_steps, instead of every env step
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self.gamma = cfg.train.gamma
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# Wwarm up period for critic before actor updates
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self.n_critic_warmup_itr = cfg.train.n_critic_warmup_itr
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# Optimizer
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self.actor_optimizer = torch.optim.AdamW(
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self.model.actor.parameters(),
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lr=cfg.train.actor_lr,
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weight_decay=cfg.train.actor_weight_decay,
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)
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self.actor_lr_scheduler = CosineAnnealingWarmupRestarts(
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self.actor_optimizer,
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first_cycle_steps=cfg.train.actor_lr_scheduler.first_cycle_steps,
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cycle_mult=1.0,
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max_lr=cfg.train.actor_lr,
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min_lr=cfg.train.actor_lr_scheduler.min_lr,
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warmup_steps=cfg.train.actor_lr_scheduler.warmup_steps,
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gamma=1.0,
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)
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self.critic_optimizer = torch.optim.AdamW(
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self.model.critic.parameters(),
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lr=cfg.train.critic_lr,
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weight_decay=cfg.train.critic_weight_decay,
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)
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self.critic_lr_scheduler = CosineAnnealingWarmupRestarts(
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self.critic_optimizer,
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first_cycle_steps=cfg.train.critic_lr_scheduler.first_cycle_steps,
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cycle_mult=1.0,
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max_lr=cfg.train.critic_lr,
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min_lr=cfg.train.critic_lr_scheduler.min_lr,
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warmup_steps=cfg.train.critic_lr_scheduler.warmup_steps,
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gamma=1.0,
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)
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# Buffer size
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self.buffer_size = cfg.train.buffer_size
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# Reward exponential
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self.beta = cfg.train.beta
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# Max weight for AWR
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self.max_adv_weight = cfg.train.max_adv_weight
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# Scaling reward
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self.scale_reward_factor = cfg.train.scale_reward_factor
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# Updates
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self.replay_ratio = cfg.train.replay_ratio
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self.critic_update_ratio = cfg.train.critic_update_ratio
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def run(self):
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# make a FIFO replay buffer for obs, action, and reward
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obs_buffer = deque(maxlen=self.buffer_size)
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action_buffer = deque(maxlen=self.buffer_size)
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reward_buffer = deque(maxlen=self.buffer_size)
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terminated_buffer = deque(maxlen=self.buffer_size)
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# Start training loop
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timer = Timer()
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run_results = []
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cnt_train_step = 0
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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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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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# if done at the end of last iteration, the envs are just reset
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firsts_trajs[0] = done_venv
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reward_trajs = np.zeros((self.n_steps, self.n_envs))
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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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cond = {
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"state": torch.from_numpy(prev_obs_venv["state"])
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.float()
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.to(self.device)
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}
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samples = (
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self.model(
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cond=cond,
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deterministic=eval_mode,
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)
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.cpu()
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.numpy()
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)
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action_venv = samples[:, : self.act_steps]
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# Apply multi-step action
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obs_venv, reward_venv, terminated_venv, truncated_venv, info_venv = (
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self.venv.step(action_venv)
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)
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done_venv = terminated_venv | truncated_venv
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reward_trajs[step] = reward_venv
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firsts_trajs[step + 1] = done_venv
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# add to buffer
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if not eval_mode:
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obs_buffer.append(prev_obs_venv["state"])
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action_buffer.append(action_venv)
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reward_buffer.append(reward_venv * self.scale_reward_factor)
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terminated_buffer.append(terminated_venv)
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# update for next step
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prev_obs_venv = obs_venv
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# count steps --- not acounting for done within action chunk
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cnt_train_step += self.n_envs * self.act_steps if not eval_mode else 0
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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 models
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if not eval_mode:
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obs_trajs = np.array(deepcopy(obs_buffer)) # assume only state
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reward_trajs = np.array(deepcopy(reward_buffer))
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terminated_trajs = np.array(deepcopy(terminated_buffer))
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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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values_trajs = np.array(
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self.model.critic({"state": obs_t}).detach().cpu().numpy()
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).reshape(-1, self.n_envs)
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td_trajs = td_values(
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obs_trajs, reward_trajs, terminated_trajs, values_trajs
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)
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td_t = torch.from_numpy(td_trajs.flatten()).float().to(self.device)
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# Update critic
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num_batch = int(
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self.n_steps * self.n_envs / self.batch_size * self.replay_ratio
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)
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for _ in range(num_batch // self.critic_update_ratio):
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inds = np.random.choice(len(obs_trajs), self.batch_size)
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loss_critic = self.model.loss_critic(
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{"state": obs_t[inds]}, td_t[inds]
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)
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self.critic_optimizer.zero_grad()
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loss_critic.backward()
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self.critic_optimizer.step()
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# Update policy - use a new copy of data
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obs_trajs = np.array(deepcopy(obs_buffer))
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samples_trajs = np.array(deepcopy(action_buffer))
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reward_trajs = np.array(deepcopy(reward_buffer))
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terminated_trajs = np.array(deepcopy(terminated_buffer))
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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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values_trajs = np.array(
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self.model.critic({"state": obs_t}).detach().cpu().numpy()
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).reshape(-1, self.n_envs)
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td_trajs = td_values(
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obs_trajs, reward_trajs, terminated_trajs, values_trajs
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)
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advantages_trajs = td_trajs - values_trajs
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# flatten
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obs_trajs = einops.rearrange(
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obs_trajs,
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"s e h d -> (s e) h d",
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)
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samples_trajs = einops.rearrange(
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samples_trajs,
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"s e h d -> (s e) h d",
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)
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advantages_trajs = einops.rearrange(
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advantages_trajs,
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"s e -> (s e)",
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)
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for _ in range(num_batch):
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# Sample batch
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inds = np.random.choice(len(obs_trajs), self.batch_size)
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obs_b = {
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"state": torch.from_numpy(obs_trajs[inds])
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.float()
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.to(self.device)
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}
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actions_b = (
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torch.from_numpy(samples_trajs[inds]).float().to(self.device)
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)
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advantages_b = (
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torch.from_numpy(advantages_trajs[inds]).float().to(self.device)
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)
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advantages_b = (advantages_b - advantages_b.mean()) / (
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advantages_b.std() + 1e-6
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)
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advantages_b_scaled = torch.exp(self.beta * advantages_b)
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advantages_b_scaled.clamp_(max=self.max_adv_weight)
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# Update policy with collected trajectories
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loss_actor = self.model.loss(
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actions_b,
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obs_b,
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advantages_b_scaled.detach(),
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)
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self.actor_optimizer.zero_grad()
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loss_actor.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.parameters(), self.max_grad_norm
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)
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self.actor_optimizer.step()
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# Update lr
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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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"step": cnt_train_step,
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}
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)
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if self.itr % self.log_freq == 0:
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time = timer()
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run_results[-1]["time"] = time
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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}: step {cnt_train_step:8d} | loss actor {loss_actor:8.4f} | reward {avg_episode_reward:8.4f} | t:{time: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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"total env step": cnt_train_step,
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"loss - actor": loss_actor,
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"loss - critic": loss_critic,
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"avg episode reward - train": avg_episode_reward,
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"num episode - train": num_episode_finished,
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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]["train_episode_reward"] = avg_episode_reward
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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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