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 9501–9525 of 15113 papers

TitleStatusHype
Regret Bounds for Discounted MDPs—0
Regret Bounds for Information-Directed Reinforcement Learning—0
Regret Bounds for Learning State Representations in Reinforcement Learning—0
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents—0
Regret Bounds for Reinforcement Learning via Markov Chain Concentration—0
Regret Bounds for Reinforcement Learning with Policy Advice—0
Regret Bounds for Risk-Sensitive Reinforcement Learning—0
Regret-Free Reinforcement Learning for LTL Specifications—0
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function—0
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability—0
Compositional Transfer in Hierarchical Reinforcement Learning—0
Regularized Inverse Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning—0
Regularized Policies are Reward Robust—0
Regularized Policy Iteration—0
Regularized Q-learning—0
Regularizing Action Policies for Smooth Control with Reinforcement Learning—0
Regularizing Trajectory Optimization with Denoising Autoencoders—0
Regulating Reward Training by Means of Certainty Prediction in a Neural Network-Implemented Pong Game—0
REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
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Benchmark Results

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