Multistep returns, such as $ n $-step returns and $\lambda $-returns, are commonly used to
improve the sample efficiency of reinforcement learning (RL) methods. The variance of the …

In this paper, we prove that Distributional Reinforcement Learning (DistRL), which learns the
return distribution, can obtain second-order bounds in both online and offline RL in general …

We introduce a new stage reward estimator named the long $ N $-step surrogate stage
(LNSS) reward for deep reinforcement learning (RL). It aims at mitigating the high variance …

Temporal-difference-based deep-reinforcement learning methods have typically been
driven by off-policy, bootstrap Q-Learning updates. In this paper, we investigate the effects of …

In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …

Our understanding of reinforcement learning (RL) has been shaped by theoretical and
empirical results that were obtained decades ago using tabular representations and linear …

A popular perspective in Reinforcement learning (RL) casts the problem as probabilistic
inference on a graphical model of the Markov decision process (MDP). The core object of …

Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a
potentially discontinuous value function, and generalizing well to new observations. In this …

This paper advocates the use of offline (batch) reinforcement learning (RL) to help (1) isolate
the contributions of exploitation vs. exploration in off-policy deep RL,(2) improve …

Diffusion models have emerged as a prominent class of generative models, surpassing
previous methods regarding sample quality and training stability. Recent works have shown …