Intrinsic Reward Policy Optimization for Sparse-Reward Environments
This addresses exploration challenges for reinforcement learning agents in sparse-reward settings, representing an incremental improvement over existing methods.
The paper tackles the problem of exploration in sparse-reward reinforcement learning by proposing a policy optimization framework that uses multiple intrinsic rewards to directly optimize for extrinsic rewards, achieving improved performance and sample efficiency compared to baselines in discrete and continuous environments.
Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient. Intrinsic rewards can also provide principled guidance for exploration by, for example, combining them with extrinsic rewards to optimize a policy or using them to train subpolicies for hierarchical learning. However, the former approach suffers from unstable credit assignment, while the latter exhibits sample inefficiency and sub-optimality. We propose a policy optimization framework that leverages multiple intrinsic rewards to directly optimize a policy for an extrinsic reward without pretraining subpolicies. Our algorithm -- intrinsic reward policy optimization (IRPO) -- achieves this by using a surrogate policy gradient that provides a more informative learning signal than the true gradient in sparse-reward environments. We demonstrate that IRPO improves performance and sample efficiency relative to baselines in discrete and continuous environments, and formally analyze the optimization problem solved by IRPO. Our code is available at https://github.com/Mgineer117/IRPO.