Validation & Inference

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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.

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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:

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