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 3901–3950 of 15113 papers

TitleStatusHype
A Study of Plasticity Loss in On-Policy Deep Reinforcement LearningCode0
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning—0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
Large Language Model-Driven Curriculum Design for Mobile NetworksCode0
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM AlignmentCode0
LeDex: Training LLMs to Better Self-Debug and Explain Code—0
Extreme Value Monte Carlo Tree Search—0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective—0
Highway Reinforcement Learning—0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
Mollification Effects of Policy Gradient Methods—0
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation—0
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement LearningCode0
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments—0
Oracle-Efficient Reinforcement Learning for Max Value Ensembles—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
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
Competing for pixels: a self-play algorithm for weakly-supervised segmentationCode0
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS—0
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling—0
Constrained Ensemble Exploration for Unsupervised Skill Discovery—0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Embedding-Aligned Language Models—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
Model-free reinforcement learning with noisy actions for automated experimental control in opticsCode0
Offline Reinforcement Learning from Datasets with Structured Non-StationarityCode0
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality—0
Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of ExperiencesCode0
Variational Delayed Policy OptimizationCode0
Exclusively Penalized Q-learning for Offline Reinforcement Learning—0
A finite time analysis of distributed Q-learning—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks—0
Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
Learning to sample fibers for goodness-of-fit testing—0
Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systemsCode0
Leader Reward for POMO-Based Neural Combinatorial Optimization—0
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model—0
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Benchmark Results

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