fix train.py, stats, eval.py (training is running)

lerobot/scripts/train.py

@@ -145,7 +145,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
     #     )
 
     logging.info("make_env")
-    env = make_env(cfg)
+    env = make_env(cfg, num_parallel_envs=cfg.eval_episodes)
 
     logging.info("make_policy")
     policy = make_policy(cfg)
@@ -173,12 +173,11 @@ def train(cfg: dict, out_dir=None, job_name=None):
             eval_info, first_video = eval_policy(
                 env,
                 policy,
-                num_episodes=cfg.eval_episodes,
-                max_steps=cfg.env.episode_length,
                 return_first_video=True,
                 video_dir=Path(out_dir) / "eval",
                 save_video=True,
                 transform=dataset.transform,
+                seed=cfg.seed,
             )
             log_eval_info(logger, eval_info["aggregated"], step, cfg, dataset, is_offline)
             if cfg.wandb.enable:
@@ -211,7 +210,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
         for key in batch:
             batch[key] = batch[key].to(cfg.device, non_blocking=True)
 
-        train_info = policy.update(batch, step)
+        train_info = policy(batch, step)
 
         # TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
         if step % cfg.log_freq == 0:
@@ -223,6 +222,8 @@ def train(cfg: dict, out_dir=None, job_name=None):
 
         step += 1
 
+    raise NotImplementedError()
+
     demo_buffer = dataset if cfg.policy.balanced_sampling else None
     online_step = 0
     is_offline = False