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 3571–3580 of 15113 papers

TitleStatusHype
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
Forward KL Regularized Preference Optimization for Aligning Diffusion Policies—0
Semifactual Explanations for Reinforcement LearningCode0
Markov Chain Variance Estimation: A Stochastic Approximation Approach—0
An Introduction to Quantum Reinforcement Learning (QRL)—0
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design—0
Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks—0
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions—0
Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments—0
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Benchmark Results

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