Context-switching and adaptation: Brain-inspired mechanisms for handling environmental changes

Eric Chalmers, Edgar Bermudez Contreras, Brandon Robertson, Artur Luczak, Aaron J. Gruber

Abstract: Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it interacts with its environment. Conventional model-based RL algorithms learn rapidly, but can be slow to adapt to sudden changes in the environment. Animals' brains, however, are thought to employ model-based RL mechanisms for learning, but are able to adapt to changes with relative ease. By employing “transfer learning”, they can recycle previously learned information to solve new problems with minimal new learning. We developed two brain-inspired methods that can allow model-based RL to cope with changes to the underlying process being learned: hierarchical state abstraction, and context-switching. Hierarchical state abstraction allows a previously-learned model to be efficiently adapted for use in a new task, while context switching allows learned models to be saved and recalled at the appropriate times. We test these mechanisms using grid-world simulations in which the goal remains constant, but contingencies for reaching it frequently change. These mechanisms allow an agent to significantly outperform a conventional model-based RL algorithm in the task.