Reinforcement learning of multiple tasks using parametric bias

We propose a reinforcement learning system designed to learn multiple different continuous state-action-space tasks. The system has been tested on a family of space-searching task akin to Morris water maze, but with obstacles. While exploring a task, the agent builds its internal model of the environment and approximates a state value function. For learning multiple tasks, we use a parametric bias switching mechanism in which the value of the parametric bias layer identifies the task for the agent. Each task has a specific parametric bias vector, and during training the vectors self-organize to reflect the structure of relationships between tasks in the task set. This mapping of the task set to parametric bias space can later be used to generate novel behaviors of the agent.