Reinforcement Learning

Also known as RL

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144 papers in the last four weeks, up 243% on the four weeks before. 1.4% of all new papers.

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Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents
Dec 6, 2024Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad ShafiqueReinforcement Learning Deep Q-Learning
eBRAIN Lab · New York University (NYU) Abu Dhabi
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy
Dec 5, 2024Hongming Li, Zhao Yang, Xiaoxuan Liang +2Intrinsic Reward Methods Reinforcement Learning
Zhejiang Lab, Huangzhou, China · Vrije Universiteit Amsterdam, Amsterdam, the Netherlands · Computational NeuroEngineering Laboratory at the University of Florida, Gainesville, USA
Reinforcement learning for Quantum Tiq-Taq-Toe
Nov 10, 2024Catalin-Viorel Dinu, Thomas MoerlandReinforcement Learning Game-Playing Agents
Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands
RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
Oct 31, 2024Fu-Chieh Chang, Yu-Ting Lee, Hui-Ying Shih +2Reinforcement Learning RL for Language Model Reasoning
MediaTek Research, Taipei, Taiwan · Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · Department of Mathematical Sciences, National Chengchi University, Taipei, Taiwan +2
Streaming Deep Reinforcement Learning Finally Works
Oct 18, 2024Mohamed Elsayed, Elena Sorina Lupu, Gautham Vasan +1Reinforcement Learning Actor-Critic Methods
Department of Computing Science, University of Alberta, Edmonton, Canada · Alberta Machine Intelligence Institute (Amii) · Canada CIFAR AI Chair
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
Oct 4, 2024Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan +1Reinforcement Learning RL Exploration
Delft University of Technology, Delft, The Netherlands
Deep Reinforcement Learning for Reach-Avoid-Stay Problems
Oct 3, 2024Gabriel Chenevert, Jingqi Li, Achyuta kannan +2Robust Control Reinforcement Learning
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, USA · Department of Aerospace Engineering and Engineering Mechanics, at University of Texas, Austin, USA · Department of Computer Science, North Carolina State University, Raleigh, USA +1
Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning
Sep 22, 2024Jia Wan, Sean R. Sinclair, Devavrat Shah +1Regret Minimization in RL Markov Decision Processes
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology · Department of Industrial Engineering and Management Sciences, Northwestern University
Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion
Aug 19, 2024Achref Jaziri, Etienne Künzel, Visvanathan RameshStability-Plasticity Dilemma Non-Stationary RL
Department of Computer Science and Mathematics, Goethe University · HessianAI, Frankfurt am Main, Germany
Training Verifiably Robust Agents Using Set-Based Reinforcement Learning
Aug 17, 2024Manuel Wendl, Lukas Koller, Tobias Ladner +1RL Control Reinforcement Learning
Technical University of Munich, Germany
ProSpec RL: Plan Ahead, then Execute
Jul 31, 2024Liangliang Liu, Yi Guan, BoRan Wang +5Reinforcement Learning Risk-Sensitive RL
1The Faculty of Computing, Harbin Institute of Technology, Harbin, China · 2The Artificial Intelligence Institute, Harbin Institute of Technology, ShenZhen, China · 3The Artificial Intelligence Institute, Harbin Institute of Technology, Harbin, China
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
Jul 21, 2024Sheila Schoepp, Mehran Taghian, Shotaro Miwa +3RL Control Reinforcement Learning
Department of Computing Science, University of Alberta, 8900 114 St NW, Edmonton, Alberta, T6G 2S4, Canada · Advanced Technology R&D Center, Mitsubishi Electric Corporation, 8-1-1,Tsukaguchi-honmachi, Amagasaki-shi, Hyogo, 661-8661, Japan · Information Technology R&D Center, Mitsubishi Electric Corporation, 5-1-1, Ofuna, Kamakura-shi, Kanagawa, 247-8501, Japan +1
Thompson Sampling for Infinite-Horizon Discounted Decision Processes
May 14, 2024Daniel Adelman, Cagla Keceli, Alba V. Olivares-NadalThompson Sampling Regret Minimization in RL
Booth School of Business, The University of Chicago, Chicago, IL 60637, USA · United Airlines, Chicago, IL 60605, USA · UNSW Business School, UNSW, Kensington NSW 2033, Australia
TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning
Jan 21, 2024Charles Westphal, Stephen Hailes, Mirco MusolesiReinforcement Learning State Representation Learning
UCL Centre for Artificial Intelligence Department of Computer Science University College London Gower Street, London WC1E 6BT, United Kingdom · Department of Computer Science University College London Gower Street, London WC1E 6BT, United Kingdom · Department of Computer Science and Engineering University of Bologna Viale Del Risorgimento 2, 40136 Bologna, Italy
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Nov 16, 2023Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18RL Benchmarks Reinforcement Learning
University of Oxford · Universitat Politècnica de Catalunya · University College London +2
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Oct 11, 2023Zeyang Li, Chuxiong Hu, Yunan Wang +4Markov Decision Processes Reinforcement Learning
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, United States · Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China · College of Artificial Intelligence and the School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip
Jul 29, 2023Saki Omi, Hyo-Sang Shin, Namhoon Cho +2RL for Robotics Reinforcement Learning
Space Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg · School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, U.K. · Department of Aerospace Engineering, Seoul National University, Seoul, South Korea
ETHER: Aligning Emergent Communication for Hindsight Experience Replay
Jul 28, 2023Kevin Yandoka Denamganaï, Daniel Hernandez, Ozan Vardal +2Reinforcement Learning Experience Replay
Department of Computer Science University of York York, UK
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Jun 23, 2023Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione +5Reinforcement Learning Causal RL
Arizona State University, Arizona, USA · Max Planck Institute for Software Systems, Kaiserslautern, Germany · University of Texas at Austin, Texas, USA +3
Topology-Guided Modular Actor-Critic Learning for Continuous Systems under Temporal Objectives
Apr 20, 2023Lening Li, Zhentian Qian, Jianan Xia +7RL Control Reinforcement Learning
Ningbo University, Ningbo, China · Robotics Engineering Program, Worcester Polytechnic Institute, Worcester, MA, USA · Ningbo Zhenhai Silver-Ball Bearing Co., Ltd., Ningbo, China
Playing 20 Question Game with Policy-Based Reinforcement Learning
Aug 23, 2018Huang Hu, Xianchao Wu, Bingfeng Luo +4Reinforcement Learning Game-Playing Agents
1Peking University, Beijing, China · 3Microsoft Development Co., Ltd, Tokyo, Japan · 2Microsoft Corporation, Beijing, China
Safe Learning Under Irreversible Dynamics via Asking for Help
Date pendingBenjamin Plaut, Juan Liévano-Karim, Hanlin Zhu +1Regret Minimization in RL Reinforcement Learning
Division of Computer Science University of California Berkeley, CA 94720-1776, USA
AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training
Date pendingZhenyu Han, Ansheng You, Haibo Wang +16RL for Language Models Asynchronous RL
Huawei · Individual Researcher
UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors
Date pendingAtahan Cilan, Atay ÖzgövdeReinforcement Learning Human Behavior Simulation
Turkish Aerospace, Istanbul, Turkey · Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey
Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
Date pendingXuan Yu, Xu Wang, Rui Zhu +2Reinforcement Learning Generative Flow Networks
University of Science and Technology of China (USTC), Hefei, China · Suzhou Institute for Advanced Research, USTC, Suzhou, China · State Key Laboratory of Precision and Intelligent Chemistry, USTC, Hefei, China
Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning
Date pendingZikang Shan, Han Zhong, Liwei Wang +1RL for Language Models Reinforcement Learning
Peking University · Microsoft Research Asia · Shanghai Jiao Tong University
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Date pendingPhilip Jordan, Maryam KamgarpourNash Equilibrium Reinforcement Learning
SYCAMORE, EPFL
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Date pendingAnthony GX-Chen, Ankit Anand, Gheorghe Comanici +7Reinforcement Learning RL for Language Model Reasoning
New York University · Google DeepMind

Dec 6, 2024, cs.NEJ/K move · Enter open · S save

Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents

Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad Shafique

eBRAIN Lab · New York University (NYU) Abu Dhabi

Reinforcement Learning Deep Q-Learning Spiking Neural Networks Multi-Task RL

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Reinforcement learning (RL) has demonstrated remarkable capabilities in training agents to solve complex tasks autonomously, such as mobile robots, UAVs/UGVs, and game-playing agents). However, scaling RL to master multiple tasks simultaneously (i.e., so-called multi-task RL) remains a significant challenge. Such a multi-task RL capability especially is important for agents to adapt to changes in real-world operational environments. State-of-the-art works show that, training agents with neural networks and shared structures across tasks promises improved generalization in simultaneous multi-task RL. However, they still suffer from task interference and incur high energy consumption due to intensive computation. To address this, we propose MTSpark, a novel methodology that enables energy-efficient simultaneous multi-task RL using spiking neural networks (SNNs) equipped with active dendrites for bio-inspired generalist agents. Specifically, MTSpark enhances a Deep Spiking Q-Network (DSQN) with active dendrites, a dueling structure, and task-specific context signals to dynamically form specialized sub-networks for individual tasks, while exploiting sparse operations for energy-efficient network processing. Experimental results demonstrate that MTSpark achieves higher performance and efficiency compared to state-of-the-art by obtaining high scores across three Atari games (i.e., Pong: -5.4, Breakout: 0.6, and Enduro: 371.2), approaching human-level performance (i.e., Pong: -3, Breakout: 31, Enduro: 368), while incurring similar memory and about 2x lower energy than state-of-the-art. These results show that our MTSpark potentially advances the frontiers toward energy-efficient generalist agents by combining RL and SNNs.

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Latest papers 1,138