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 3901–3925 of 15113 papers

TitleStatusHype
A Study of Plasticity Loss in On-Policy Deep Reinforcement LearningCode0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning—0
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning—0
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective—0
LeDex: Training LLMs to Better Self-Debug and Explain Code—0
Extreme Value Monte Carlo Tree Search—0
Highway Reinforcement Learning—0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Large Language Model-Driven Curriculum Design for Mobile NetworksCode0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
Mollification Effects of Policy Gradient Methods—0
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM AlignmentCode0
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement LearningCode0
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation—0
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model ScalesCode0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Oracle-Efficient Reinforcement Learning for Max Value Ensembles—0
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments—0
Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld—0
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning—0
Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement LearningCode0
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
Competing for pixels: a self-play algorithm for weakly-supervised segmentationCode0
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS—0
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Benchmark Results

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