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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2451–2475 of 15113 papers

TitleStatusHype
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Code as Reward: Empowering Reinforcement Learning with VLMs—0
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs—0
Learning Diverse Policies with Soft Self-Generated Guidance—0
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy—0
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model FeedbackCode2
Averaging n-step Returns Reduces Variance in Reinforcement Learning—0
SEABO: A Simple Search-Based Method for Offline Imitation LearningCode1
Reinforcement Learning from Bagged Reward—0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
No-Regret Reinforcement Learning in Smooth MDPs—0
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Abstracted Trajectory Visualization for Explainability in Reinforcement Learning—0
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences—0
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance—0
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment DesignCode0
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning—0
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement LearningCode3
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning—0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human ElbowsCode0
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short DelaysCode0
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Benchmark Results

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