Bootstrapping Deep RL with Population-Based Diversity Search
Standard deep RL algorithms using continuous actions suffer from inefficient exploration when facing sparse or deceptive reward problems. Here we propose to decouple exploration and exploitation. An exploration algorithm first optimizes for diversity in the space of behaviors. Then, a state-of-the art deep RL algorithm uses the collected trajectories for bootstrapping.