Rename entropy_loss to ent_loss

scripts/cleanba.py

@@ -25,7 +25,7 @@ from tensorboardX import SummaryWriter
 
 from ygoai.utils import init_ygopro, load_embeddings
 from ygoai.rl.ckpt import ModelCheckpoint, sync_to_gcs, zip_files
-from ygoai.rl.jax.agent2 import RNNAgent, ModelArgs
+from ygoai.rl.jax.agent import RNNAgent, ModelArgs
 from ygoai.rl.jax.utils import RecordEpisodeStatistics, masked_normalize, categorical_sample
 from ygoai.rl.jax.eval import evaluate, battle
 from ygoai.rl.jax import clipped_surrogate_pg_loss, vtrace_2p0s, mse_loss, entropy_loss, simple_policy_loss, ach_loss, policy_gradient_loss
@@ -285,7 +285,7 @@ def rollout(
     avg_win_rates = deque(maxlen=1000)
     
     agent = create_agent(args)
-    eval_agent = create_agent(args, eval=True)
+    eval_agent = create_agent(args, eval=eval_mode != 'bot')
 
     @jax.jit
     def get_action(params, obs, rstate):
@@ -492,7 +492,7 @@ def rollout(
             writer.add_scalar("charts/SPS_update", SPS_update, tb_global_step)
 
 
-if __name__ == "__main__":
+def main():
     args = tyro.cli(Args)
     args.local_batch_size = int(args.local_num_envs * args.num_steps * args.num_actor_threads * len(args.actor_device_ids))
     args.local_minibatch_size = int(args.local_batch_size // args.num_minibatches)
@@ -796,13 +796,13 @@ if __name__ == "__main__":
             shuffled_mask = jnp.ones_like(shuffled_storage.mains)
 
             def update_minibatch(agent_state, minibatch):
-                (loss, (pg_loss, v_loss, entropy_loss, approx_kl)), grads = loss_grad_fn(
+                (loss, (pg_loss, v_loss, ent_loss, approx_kl)), grads = loss_grad_fn(
                     agent_state.params, *minibatch)
                 grads = jax.lax.pmean(grads, axis_name="local_devices")
                 agent_state = agent_state.apply_gradients(grads=grads)
-                return agent_state, (loss, pg_loss, v_loss, entropy_loss, approx_kl)
+                return agent_state, (loss, pg_loss, v_loss, ent_loss, approx_kl)
 
-            agent_state, (loss, pg_loss, v_loss, entropy_loss, approx_kl) = jax.lax.scan(
+            agent_state, (loss, pg_loss, v_loss, ent_loss, approx_kl) = jax.lax.scan(
                 update_minibatch,
                 agent_state,
                 (
@@ -819,17 +819,17 @@ if __name__ == "__main__":
                     shuffled_next_value,
                 ),
             )
-            return (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl)
+            return (agent_state, key), (loss, pg_loss, v_loss, ent_loss, approx_kl)
 
-        (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl) = jax.lax.scan(
+        (agent_state, key), (loss, pg_loss, v_loss, ent_loss, approx_kl) = jax.lax.scan(
             update_epoch, (agent_state, key), (), length=args.update_epochs
         )
         loss = jax.lax.pmean(loss, axis_name="local_devices").mean()
         pg_loss = jax.lax.pmean(pg_loss, axis_name="local_devices").mean()
         v_loss = jax.lax.pmean(v_loss, axis_name="local_devices").mean()
-        entropy_loss = jax.lax.pmean(entropy_loss, axis_name="local_devices").mean()
+        ent_loss = jax.lax.pmean(ent_loss, axis_name="local_devices").mean()
         approx_kl = jax.lax.pmean(approx_kl, axis_name="local_devices").mean()
-        return agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key
+        return agent_state, loss, pg_loss, v_loss, ent_loss, approx_kl, key
 
     all_reduce_value = jax.pmap(
         lambda x: jax.lax.pmean(x, axis_name="main_devices"),
@@ -872,7 +872,6 @@ if __name__ == "__main__":
             params_queues[-1].put(device_params)
 
     rollout_queue_get_time = deque(maxlen=10)
-    data_transfer_time = deque(maxlen=10)
     learner_policy_version = 0
     while True:
         learner_policy_version += 1
@@ -905,7 +904,7 @@ if __name__ == "__main__":
 
         rollout_queue_get_time.append(time.time() - rollout_queue_get_time_start)
         training_time_start = time.time()
-        (agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, learner_keys) = multi_device_update(
+        (agent_state, loss, pg_loss, v_loss, ent_loss, approx_kl, learner_keys) = multi_device_update(
             agent_state,
             *list(zip(*sharded_data_list)),
             learner_keys,
@@ -943,7 +942,7 @@ if __name__ == "__main__":
             )
             writer.add_scalar("losses/value_loss", v_loss[-1].item(), tb_global_step)
             writer.add_scalar("losses/policy_loss", pg_loss[-1].item(), tb_global_step)
-            writer.add_scalar("losses/entropy", entropy_loss[-1].item(), tb_global_step)
+            writer.add_scalar("losses/entropy", ent_loss[-1].item(), tb_global_step)
             writer.add_scalar("losses/approx_kl", approx_kl[-1].item(), tb_global_step)
             writer.add_scalar("losses/loss", loss, tb_global_step)
 
@@ -965,4 +964,8 @@ if __name__ == "__main__":
     if args.distributed:
         jax.distributed.shutdown()
 
-    writer.close()
\ No newline at end of file
+    writer.close()
+
+
+if __name__ == "__main__":
+    main()