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 2031–2040 of 15113 papers

TitleStatusHype
Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld—0
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step OptimizationCode1
Competing for pixels: a self-play algorithm for weakly-supervised segmentationCode0
Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement LearningCode0
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning—0
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS—0
Constrained Ensemble Exploration for Unsupervised Skill Discovery—0
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous controlCode2
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy OptimizationCode2
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Benchmark Results

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