hopefully the final change

cleanrl/ppo_continuous_action_wandb.py

@@ -302,7 +302,7 @@ def train(cfg):
     next_obs, _ = envs.reset(seed=cfg.seed)
     next_obs = torch.Tensor(next_obs).to(device)
     next_done = torch.zeros(cfg.num_envs).to(device)
-    num_updates = cfg.total_timesteps // batch_size
+    num_updates = cfg.total_timesteps // cfg.batch_size
 
     cum_cost, ep_cost, ep_risk_cost_int, cum_risk_cost_int, ep_risk, cum_risk = 0, 0, 0, 0, 0, 0
     cost = 0
@@ -452,12 +452,12 @@ def train(cfg):
         b_risks = risks.reshape((-1, ) + (2, ))
 
         # Optimizing the policy and value network
-        b_inds = np.arange(batch_size)
+        b_inds = np.arange(cfg.batch_size)
         clipfracs = []
         for epoch in range(cfg.update_epochs):
             np.random.shuffle(b_inds)
-            for start in range(0, batch_size, minibatch_size):
-                end = start + minibatch_size
+            for start in range(0, cfg.batch_size, cfg.minibatch_size):
+                end = start + cfg.minibatch_size
                 mb_inds = b_inds[start:end]
 
                 if cfg.use_risk:
@@ -506,7 +506,7 @@ def train(cfg):
                 nn.utils.clip_grad_norm_(agent.parameters(), cfg.max_grad_norm)
                 optimizer.step()
 
-            if cfg.target_kl != "None":
+            if cfg.target_kl is not  None:
                 if approx_kl > cfg.target_kl:
                     break
 
@@ -550,4 +550,4 @@ def train(cfg):
 
 if __name__ == "__main__":
     cfg = parse_args()
-    train(cfg)
+    train(cfg)