SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2801–2850 of 15113 papers

TitleStatusHype
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning—0
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator—0
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning—0
AutoDOViz: Human-Centered Automation for Decision Optimization—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure—0
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
Adaptive Discounting of Training Time Attacks—0
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options—0
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning—0
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search—0
A User Study on Explainable Online Reinforcement Learning for Adaptive Systems—0
A bandit approach to curriculum generation for automatic speech recognition—0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
A Unifying View of Optimism in Episodic Reinforcement Learning—0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
A unified view of entropy-regularized Markov decision processes—0
A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty—0
A unified strategy for implementing curiosity and empowerment driven reinforcement learning—0
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning—0
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning—0
AARL: Automated Auxiliary Loss for Reinforcement Learning—0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
A Language Model based Evaluator for Sentence Compression—0
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment—0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning—0
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning—0
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified