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 2251–2275 of 15113 papers

TitleStatusHype
Multiagent Cooperation and Competition with Deep Reinforcement LearningCode1
Prioritized Experience ReplayCode1
Deep Reinforcement Learning in Parameterized Action SpaceCode1
Deep Reinforcement Learning with Double Q-learningCode1
Continuous control with deep reinforcement learningCode1
Giraffe: Using Deep Reinforcement Learning to Play ChessCode1
Weight Uncertainty in Neural NetworksCode1
Optimizing the CVaR via SamplingCode1
Scalable Planning and Learning for Multiagent POMDPs: Extended VersionCode1
Off-Policy General Value Functions to Represent Dynamic Role Assignments in RoboCup 3D Soccer SimulationCode1
Playing Atari with Deep Reinforcement LearningCode1
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink PaintingCode1
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning—0
VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks—0
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback—0
QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation—0
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)—0
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities—0
Kevin: Multi-Turn RL for Generating CUDA Kernels—0
Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models—0
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training—0
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning—0
Exploring the robustness of TractOracle methods in RL-based tractographyCode0
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light—0
Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction—0
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Benchmark Results

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