Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning
This work provides incremental insights into developing more efficient exploration strategies for reinforcement learning in environments with rare events.
The paper tackled the problem of efficient exploration in reinforcement learning with unknown stochastic dynamics and sparse rewards, finding that parallel simulations exhibit a phase transition in reaching rare states and identifying an optimal number of simulations to balance exploration diversity and time allocation, with a restarting mechanism exponentially enhancing success probability.
We investigate efficient exploration strategies of environments with unknown stochastic dynamics and sparse rewards. Specifically, we analyze first the impact of parallel simulations on the probability of reaching rare states within a finite time budget. Using simplified models based on random walks and Lévy processes, we provide analytical results that demonstrate a phase transition in reaching probabilities as a function of the number of parallel simulations. We identify an optimal number of parallel simulations that balances exploration diversity and time allocation. Additionally, we analyze a restarting mechanism that exponentially enhances the probability of success by redirecting efforts toward more promising regions of the state space. Our findings contribute to a more qualitative and quantitative theory of some exploration schemes in reinforcement learning, offering insights into developing more efficient strategies for environments characterized by rare events.