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 7851–7875 of 15113 papers

TitleStatusHype
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning—0
On Reinforcement Learning, Effect Handlers, and the State Monad—0
On Reinforcement Learning for Full-length Game of StarCraft—0
On Reinforcement Learning for Turn-based Zero-sum Markov Games—0
On Representation Complexity of Model-based and Model-free Reinforcement Learning—0
On Reward-Free Reinforcement Learning with Linear Function Approximation—0
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game—0
On Reward Function for Survival—0
On Reward Maximization and Distribution Matching for Fine-Tuning Language Models—0
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach—0
On-Robot Reinforcement Learning with Goal-Contrastive Rewards—0
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond—0
OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning—0
On solutions of the distributional Bellman equation—0
On Solving Cooperative MARL Problems with a Few Good Experiences—0
A framework for online, stabilizing reinforcement learning—0
On the Analysis of Computational Delays in Reinforcement Learning-based Rate Adaptation Algorithms—0
On the benefits of deep RL in accelerated MRI sampling—0
On the Benefits of Leveraging Structural Information in Planning Over the Learned Model—0
On the Complexity of Adversarial Decision Making—0
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games—0
On the Computability of AIXI—0
On the Computability of Solomonoff Induction and Knowledge-Seeking—0
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control—0
On the connection between Bregman divergence and value in regularized Markov decision processes—0
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Benchmark Results

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