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 626–650 of 15113 papers

TitleStatusHype
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement LearningCode1
Online Symbolic Music Alignment with Offline Reinforcement LearningCode1
Generalizable Visual Reinforcement Learning with Segment Anything ModelCode1
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement LearningCode1
Efficient Reinforcement Learning via Decoupling Exploration and UtilizationCode1
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio ApplicationsCode1
Challenges for Reinforcement Learning in Quantum Circuit DesignCode1
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory OptimizationCode1
Learning to Act without ActionsCode1
Active Reinforcement Learning for Robust Building ControlCode1
World Models via Policy-Guided Trajectory DiffusionCode1
The Effective Horizon Explains Deep RL Performance in Stochastic EnvironmentsCode1
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL ApproachCode1
Sequential Planning in Large Partially Observable Environments guided by LLMsCode1
The Generalization Gap in Offline Reinforcement LearningCode1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal ControlCode1
Mitigating Open-Vocabulary Caption HallucinationsCode1
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Unveiling the Implicit Toxicity in Large Language ModelsCode1
Large Language Model as a Policy Teacher for Training Reinforcement Learning AgentsCode1
Show:102550
← PrevPage 26 of 605Next →

Benchmark Results

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