Provable and practical: Efficient exploration in reinforcement learning via langevin monte carlo
We present a scalable and effective exploration strategy based on Thompson sampling for
reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling …
reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling …
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
We theoretically explore the relationship between sample-efficiency and adaptivity in
reinforcement learning. An algorithm is sample-efficient if it uses a number of queries $ n …
reinforcement learning. An algorithm is sample-efficient if it uses a number of queries $ n …