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 2526–2550 of 15113 papers

TitleStatusHype
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Active Inference as a Model of Agency—0
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning—0
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN—0
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
Solving Offline Reinforcement Learning with Decision Tree RegressionCode0
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot—0
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement LearningCode1
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
Large-scale Reinforcement Learning for Diffusion Models—0
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of ViewCode1
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough ReproductionCode0
Crowd-PrefRL: Preference-Based Reward Learning from Crowds—0
Blackout Mitigation via Physics-guided RLCode0
Deployable Reinforcement Learning with Variable Control RateCode0
DeLF: Designing Learning Environments with Foundation ModelsCode0
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender SystemsCode1
Bridging State and History Representations: Understanding Self-Predictive RLCode1
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation—0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
PRewrite: Prompt Rewriting with Reinforcement Learning—0
CycLight: learning traffic signal cooperation with a cycle-level strategy—0
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Benchmark Results

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