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 2326–2350 of 15113 papers

TitleStatusHype
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