ISL: A novel approach for deep exploration
This addresses the challenge of efficient exploration in RL for AI agents, though it appears incremental as it builds on existing methods like maximum entropy RL.
The paper tackles the problem of deep exploration in reinforcement learning by introducing the ISL algorithm, which achieves state-of-the-art performance on challenging benchmarks.
In this article we explore an alternative approach to address deep exploration and we introduce the ISL algorithm, which is efficient at performing deep exploration. Similarly to maximum entropy RL, we derive the algorithm by augmenting the traditional RL objective with a novel regularization term. A distinctive feature of our approach is that, as opposed to other works that tackle the problem of deep exploration, in our derivation both the learning equations and the exploration-exploitation strategy are derived in tandem as the solution to a well-posed optimization problem whose minimization leads to the optimal value function. Empirically we show that our method exhibits state of the art performance on a range of challenging deep-exploration benchmarks.