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 5301–5325 of 15113 papers

TitleStatusHype
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing—0
Reward-Directed Score-Based Diffusion Models via q-Learning—0
Reward Estimation via State Prediction—0
Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward—0
Reward-Free Attacks in Multi-Agent Reinforcement Learning—0
Reward-Free Exploration for Reinforcement Learning—0
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation—0
Reward-Free Policy Space Compression for Reinforcement Learning—0
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes—0
Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning—0
Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System—0
Reward Gaming in Conditional Text Generation—0
Task Aware Dreamer for Task Generalization in Reinforcement Learning—0
Rewarding Doubt: A Reinforcement Learning Approach to Confidence Calibration of Large Language Models—0
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning—0
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning—0
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation—0
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning—0
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue—0
Reward is enough for convex MDPs—0
Reward Is Enough: LLMs Are In-Context Reinforcement Learners—0
Reward is not enough: can we liberate AI from the reinforcement learning paradigm?—0
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery—0
Reward Learning using Structural Motifs in Inverse Reinforcement Learning—0
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Benchmark Results

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