1. 9.5 Large Language Models as Generalizable Policies for Embodied Tasks
   • Authors: Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Walter Talbott, Katherine Metcalf, Natalie Mackraz, Devon Hjelm, Alexander Toshev
   • This paper presents a new approach, Large LAnguage model Reinforcement Learning Policy (LLaRP), that utilizes large language models for generalizable policies in embodied visual tasks. The paper’s potential influence stems not only from the novelty of the approach but also from its robust performance under complex task instructions and novel optimal behavior.
2. 9.1 Lifting the Veil: Unlocking the Power of Depth in Q-learning
   • Authors: Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou
   • Reason: The authors have created a comprehensive theoretical study of Deep Q-learning that also includes precise conditions under which it outperforms traditional Q-learning. This research answers crucial questions about the performances of Deep Q-learning and can substantively contribute to the understanding and further development of convolutional neural networks for reinforcement learning.
3. 9.0 Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
   • Authors: Shenzhi Wang, Qisen Yang, Jiawei Gao, Matthieu Gaetan Lin, Hao Chen, Liwei Wu, Ning Jia, Shiji Song, Gao Huang
   • Reason: The proposed method addresses a key challenge in reinforcement learning (RL) with the introduction of a technique for state-adaptive improvements. The authors demonstrate that their method yields statistically significant performance enhancement, potentially initiating new research directions aiming to tackle the issue of non-stationarity in RL.
4. 8.9 Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates
   • Authors: Nicholas E. Corrado, Josiah P. Hanna
   • This paper contributes to our understanding of when data augmentation can improve data efficiency in reinforcement learning tasks, focusing on sparse-reward tasks with dynamics-invariant data augmentation. The findings could play an instrumental role in shaping future research directions in data augmentation for RL.
5. 8.9 Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models
   • Authors: Xue Yan, Yan Song, Xinyu Cui, Filippos Christianos, Haifeng Zhang, David Henry Mguni, Jun Wang
   • Reason: This paper proposes a novel bilevel framework that merges question-asking (prompting) with reasoning to guide action learning. The researchers show compelling results in demonstrating the superiority of their system. It has a great potential in advancing complex reasoning systems and task-solving frameworks using RL.
6. 8.7 Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
   • Authors: Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John P. Dickerson, Joseph Suarez
   • An important work that transfers the DreamerV3’s “tricks” to Proximal Policy Optimization (PPO). It provides extensive empirical studies and insights on the relationship between implementation tricks, improving our understanding of reward scale robustness for PPO.
7. 8.7 Improving Intrinsic Exploration by Creating Stationary Objectives
   • Authors: Roger Creus Castanyer, Joshua Romoff, Glen Berseth
   • Reason: By targeting the fundamental issue of non-stationarity in the exploration benefits of RL, the authors introduce a new optimization framework. Their solution includes a novel method for converting non-stationary rewards into stationary ones, which can be a major boost for exploration efficiency in RL.
8. 8.6 Model-free Posterior Sampling via Learning Rate Randomization
   • Authors: Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Remi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Menard
   • Reason: The authors propose a new randomized model-free algorithm for regret minimization and provide a sound analytical groundwork, which includes both a tabular MDP scenario and a metric state action space scenario. Insights from the paper could be instrumental in future RL algorithm development.
9. 8.3 Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
   • Authors: Mario Picerno, Lucas Koch, Kevin Badalian, Marius Wegener, Joschka Schaub, Charles Robert Koch, Jakob Andert
   • This paper addresses a key challenge in reinforcement learning – reducing training time by combining Transfer Learning and X-in-the-Loop simulation. The insights presented here could prove influential in further improving RL training processes.
10. 8.1 Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
    • Authors: Balhae Kim, Hyungi Lee, Juho Lee
    • The paper introduces a novel Bayesian pseudocoreset construction method that operates on a function space, addressing challenges in the space model parameters. It promises to impact future research and developments in Bayesian neural networks.