Self-Supervised Policy Adaptation during Deployment

Understand

In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment.

• However, generalization across different environments is known to be hard.
• A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal.
• Our work explores the use of self-supervision to allow the policy to continue training after deployment without using any rewards.