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 601–650 of 15113 papers

TitleStatusHype
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent PathfindingCode1
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning TechniquesCode1
Reflect-RL: Two-Player Online RL Fine-Tuning for LMsCode1
Policy Learning for Off-Dynamics RL with Deficient SupportCode1
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference AdjustmentCode1
Hybrid Inverse Reinforcement LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
QGFN: Controllable Greediness with Action ValuesCode1
Safety Filters for Black-Box Dynamical Systems by Learning Discriminating HyperplanesCode1
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
SEABO: A Simple Search-Based Method for Offline Imitation LearningCode1
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient UpdateCode1
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic ManipulationCode1
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement LearningCode1
DittoGym: Learning to Control Soft Shape-Shifting RobotsCode1
HAZARD Challenge: Embodied Decision Making in Dynamically Changing EnvironmentsCode1
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential EquationsCode1
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement LearningCode1
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of ViewCode1
Bridging State and History Representations: Understanding Self-Predictive RLCode1
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender SystemsCode1
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing ConstraintCode1
Interpretable Concept Bottlenecks to Align Reinforcement Learning AgentsCode1
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
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Benchmark Results

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