Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

Achieving efficient and scalable exploration in complex domains poses a major challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to the exploration problem offer strong formal guarantees, they are often impractical in higher dimensions due to their reliance on enumerating the state-action space. Hence, exploration in complex domains is often performed with simple epsilon-greedy methods. To achieve more efficient exploration, we develop a method for assigning exploration bonuses based on a concurrently learned model of the system dynamics. By parameterizing our learned model with a neural network, we are able to develop a scalable and efficient approach to exploration bonuses that can be applied to tasks with complex, high-dimensional state spaces. We demonstrate our approach on the task of learning to play Atari games from raw pixel inputs. In this domain, our method offers substantial improvements in exploration efficiency when compared with the standard epsilon greedy approach. As a result of our improved exploration strategy, we are able to achieve state-of-the-art results on several games that pose a major challenge for prior methods.