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 2301–2350 of 15113 papers

TitleStatusHype
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective—0
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting—0
Neural-Kernel Conditional Mean Embeddings—0
Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC—0
ViSaRL: Visual Reinforcement Learning Guided by Human Saliency—0
Horizon-Free Regret for Linear Markov Decision Processes—0
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement LearningCode0
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning—0
Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning—0
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement LearningCode0
Learning to Describe for Predicting Zero-shot Drug-Drug InteractionsCode0
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban EnvironmentsCode2
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback—0
Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter ControlCode0
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware PerspectiveCode0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts—0
(N,K)-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model—0
RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models—0
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
In-context Exploration-Exploitation for Reinforcement Learning—0
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
Distributional Successor Features Enable Zero-Shot Policy Optimization—0
PEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning—0
Enhancing Classification Performance via Reinforcement Learning for Feature Selection—0
Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques—0
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning—0
Switching the Loss Reduces the Cost in Batch Reinforcement Learning—0
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection—0
Inverse Design of Photonic Crystal Surface Emitting Lasers is a Sequence Modeling Problem—0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage—0
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning—0
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global WorkspaceCode0
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy—0
Noisy Spiking Actor Network for Exploration—0
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation—0
Why Online Reinforcement Learning is Causal—0
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical SystemsCode1
Belief-Enriched Pessimistic Q-Learning against Adversarial State PerturbationsCode0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning—0
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL—0
Language Guided Exploration for RL Agents in Text Environments—0
SplAgger: Split Aggregation for Meta-Reinforcement LearningCode1
Twisting Lids Off with Two Hands—0
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning—0
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Benchmark Results

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