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 3921–3930 of 15113 papers

TitleStatusHype
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
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
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling—0
Constrained Ensemble Exploration for Unsupervised Skill Discovery—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Embedding-Aligned Language Models—0
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Benchmark Results

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