Dynamic Subgoal-based Exploration via Bayesian Optimization
This addresses the problem of cost-efficient exploration for agents in complex navigation tasks where real-world training is necessary, representing an incremental improvement over fixed or less efficient meta-optimization methods.
The paper tackles the challenge of reinforcement learning in sparse-reward navigation environments with expensive interactions by proposing a cost-aware Bayesian optimization approach that dynamically adjusts subgoal-based exploration strategies, outperforming existing baselines across multiple problem domains and proving asymptotic near-optimality.
Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration. Motivated by complex navigation tasks that require real-world training (when cheap simulators are not available), we consider an agent that faces an unknown distribution of environments and must decide on an exploration strategy. It may leverage a series of training environments to improve its policy before it is evaluated in a test environment drawn from the same environment distribution. Most existing approaches focus on fixed exploration strategies, while the few that view exploration as a meta-optimization problem tend to ignore the need for cost-efficient exploration. We propose a cost-aware Bayesian optimization approach that efficiently searches over a class of dynamic subgoal-based exploration strategies. The algorithm adjusts a variety of levers -- the locations of the subgoals, the length of each episode, and the number of replications per trial -- in order to overcome the challenges of sparse rewards, expensive interactions, and noise. An experimental evaluation demonstrates that the new approach outperforms existing baselines across a number of problem domains. We also provide a theoretical foundation and prove that the method asymptotically identifies a near-optimal subgoal design.