# Empty checkpoint files with Tune.run
**URL:** https://discuss.ray.io/t/empty-checkpoint-files-with-tune-run/5596
**Category:** RLlib
**Created:** [March 29, 2022, 8:55pm UTC](https://discuss.ray.io/t/empty-checkpoint-files-with-tune-run/5596 "2022-03-29T20:55:27Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@luzgui](https://discuss.ray.io/u/luzgui)
#### Post date: [March 29, 2022, 8:55pm UTC](https://discuss.ray.io/t/empty-checkpoint-files-with-tune-run/5596/1 "2022-03-29T20:55:27Z")
**How severe does this issue affect your experience of using Ray?**
- High: It blocks me to complete my task.
Hello, I am using tune to perform different trials on hyperparameters with RLlib fin a custom environment
My problem is that I was supposed to save a final checkpoint per trial but although I have a folder inside each trial folder with checkpoints they are not the correct files
Specifically I am getting the following folder structure
```
*Experiment*
```
-
```
Trial*
```
-
```
Checkpoint_-00001*
```
-
```
.is_checkpoint*
```
-
```
.null_marker*
```
-
```
.tune_metadata*
```
-
```
params*
```
-
```
progress*
```
-
```
results*
```
Essentially I was hopping to have a trained agent per trial and select the best agent that I could then restore to perform actions on my environment. From my understanding, the checkpoint\_at\_end=True was supposed to save these checkpoints
Is there another way to load a trained agent apart from checkpoints?
here is my snippet
```auto
def experiment(config):
iterations = config.pop("train-iterations")
train_agent = DQNTrainer(config=config)
checkpoint = None
train_results = {}
for i in range(iterations):
train_results = train_agent.train()
tune.report(**train_results)
train_agent.stop()
config["lr"]=tune.grid_search([1e-5, 1e-4])
tuneobject=tune.run(
experiment,
config=config,
local_dir=raylog,
checkpoint_at_end=True,
checkpoint_freq=10,
name='Exp1',
checkpoint_score_attr="episode_reward_mean")
```
Thank you
---
### Author:  [@luzgui](https://discuss.ray.io/u/luzgui)
#### Post date: [March 30, 2022, 1:36pm UTC](https://discuss.ray.io/t/empty-checkpoint-files-with-tune-run/5596/2 "2022-03-30T13:36:50Z")
I solved this issue using as a trainable just a `PPOTrainer` or a `DQNTrainer` instead of the `experiment` function