Abstract
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive or sparse rewards. To tackle this problem, we present a diversity-driven approach for exploration, which can be easily combined with both off- and on-policy reinforcement learning algorithms. We show that by simply adding a distance measure regularization to the loss function, the proposed methodology significantly enhances an agent's exploratory behavior, and thus prevents the policy from being trapped in local optima. We further propose an adaptive scaling strategy to enhance the performance. We demonstrate the effectiveness of our method in huge 2D gridworlds and a variety of benchmark environments, including Atari 2600 and MuJoCo. Experimental results validate that our method outperforms baseline approaches in most tasks in terms of mean scores and exploration efficiency.