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

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Showing 2001–2050 of 15113 papers

TitleStatusHype
Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity—0
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf—0
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning—0
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning—0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
A Study of Plasticity Loss in On-Policy Deep Reinforcement LearningCode0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
LeDex: Training LLMs to Better Self-Debug and Explain Code—0
Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RLCode1
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
Reinforcement Learning in Dynamic Treatment Regimes Needs Critical ReexaminationCode1
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective—0
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM AlignmentCode0
Mollification Effects of Policy Gradient Methods—0
Highway Reinforcement Learning—0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Large Language Model-Driven Curriculum Design for Mobile NetworksCode0
Extreme Value Monte Carlo Tree Search—0
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model ScalesCode0
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments—0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Q-value Regularized Transformer for Offline Reinforcement LearningCode1
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement LearningCode0
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing ProblemsCode1
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation—0
Rethinking Transformers in Solving POMDPsCode1
Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld—0
Oracle-Efficient Reinforcement Learning for Max Value Ensembles—0
Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step OptimizationCode1
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
Competing for pixels: a self-play algorithm for weakly-supervised segmentationCode0
Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement LearningCode0
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning—0
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS—0
Constrained Ensemble Exploration for Unsupervised Skill Discovery—0
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous controlCode2
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy OptimizationCode2
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling—0
Embedding-Aligned Language Models—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
Diffusion Actor-Critic with Entropy RegulatorCode2
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Model-free reinforcement learning with noisy actions for automated experimental control in opticsCode0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
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Benchmark Results

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