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 …
Robust exploration with adversary via Langevin Monte Carlo
In the realm of Deep Q-Networks (DQNs), numerous exploration strategies have
demonstrated efficacy within controlled environments. However, these methods encounter …
demonstrated efficacy within controlled environments. However, these methods encounter …
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
We present the first study on provably efficient randomized exploration in cooperative multi-
agent reinforcement learning (MARL). We propose a unified algorithm framework for …
agent reinforcement learning (MARL). We propose a unified algorithm framework for …
On the Data Complexity of Problem-Adaptive Offline Reinforcement Learning
M Yin - 2023 - escholarship.org
Offline reinforcement learning, a field dedicated to optimizing sequential decision-making
strategies using historical data, has found widespread application in real-world scenarios …
strategies using historical data, has found widespread application in real-world scenarios …