Fast and slow curiosity for high-level exploration in reinforcement learning

Deep reinforcement learning (DRL) algorithms rely on carefully designed environment rewards that are extrinsic to the agent. However, in many real-world scenarios rewards are sparse or delayed, motivating the need for discovering efficient exploration strategies. While intrinsically motivated agents hold promise of better local exploration, solving problems that require coordinated decisions over long-time horizons remains an open problem. We postulate that to discover such strategies, a DRL agent should be able to combine local and high-level exploration behaviors. To this end, we introduce the concept of fast and slow curiosity that aims to incentivize long-time horizon exploration. Our method decomposes the curiosity bonus into a fast reward that deals with local exploration and a slow reward that encourages global exploration. We formulate this bonus as the error in an agent's ability to reconstruct the observations given their contexts. We further propose to dynamically weight local and high-level strategies by measuring state diversity. We evaluate our method on a variety of benchmark environments, including Minigrid, Super Mario Bros, and Atari games. Experimental results show that our agent outperforms prior approaches in most tasks in terms of exploration efficiency and mean scores.