Reinforcement learning algorithms commonly seek to optimize policies for solving one
particular task. How should we explore an unknown dynamical system such that the …

Policy optimization from batch data, ie, offline reinforcement learning (RL) is important when
collecting data from a current policy is not possible. This setting incurs distribution mismatch …

Expected free energy (EFE) is a central quantity in active inference which has recently
gained popularity due to its intuitive decomposition of the expected value of control into a …

This work designs and analyzes a novel set of algorithms for multi-agent reinforcement
learning (MARL) based on the principle of information-directed sampling (IDS). These …

Reinforcement learning-based policies for continuous control robotic navigation tasks often
fail to adapt to changes in the environment during real-time deployment, which may result in …

In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG)
algorithm to deal with the challenges of sparse rewards in continuous control problems …

Bandit and RL Reading Notes by Xuanfei Page 1 Bandit and RL Reading Notes by Xuanfei
Xuanfei Ren∗, Pan Xu† Abstract Here are some notes on the papers from my study. I think …