> ## Documentation Index
> Fetch the complete documentation index at: https://scaledfoundations-rebrand.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Validation & Inference

## Validation

The evaluation of the trained RL policy can be performed by setting the `train` parameter to `false` in `agent_cfg.yaml`.
This would run the agent in the training environment by utilizing the trained policy.

## Inference

Once the trained policy is validated on the training environment, it can be deployed in all the supported as well as custom environments.
Setting the `task` as `GRID-CustomEnv-v0` and specifying the environment in the `scene_cfg.yaml` enables users to use the trained policy in diverse environments.
A sample `agent_cfg.yaml` file for inference is shown below:

```yaml
- rsl_rl: 
    train: false
    experiment_name: go2_rough
    load_run: .*
    load_checkpoint: model_.*.pt
```