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 4201–4250 of 15113 papers

TitleStatusHype
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning—0
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis UseCode0
Learning to Program Variational Quantum Circuits with Fast Weights—0
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials—0
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test—0
QF-tuner: Breaking Tradition in Reinforcement Learning—0
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning—0
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function ApproximationCode0
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning—0
Reinforcement Learning with Elastic Time Steps—0
Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning—0
Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark—0
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning—0
Learning Dual-arm Object Rearrangement for Cartesian Robots—0
Dynamic Multi-Reward Weighting for Multi-Style Controllable GenerationCode0
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms—0
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning—0
Deep Hedging with Market Impact—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated PoliciesCode0
Offline Multi-task Transfer RL with Representational Penalization—0
Programmatic Reinforcement Learning: Navigating Gridworlds—0
Self-evolving Autoencoder Embedded Q-Network—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Modelling crypto markets by multi-agent reinforcement learningCode0
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation—0
Performative Reinforcement Learning in Gradually Shifting EnvironmentsCode0
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories—0
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption—0
Steady-State Error Compensation for Reinforcement Learning with Quadratic Rewards—0
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
Intelligent Agricultural Management Considering N_2O Emission and Climate Variability with Uncertainties—0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea—0
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
Auxiliary Reward Generation with Transition Distance Representation Learning—0
IR-Aware ECO Timing Optimization Using Reinforcement Learning—0
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments—0
Natural Language Reinforcement Learning—0
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF—0
RLEEGNet: Integrating Brain-Computer Interfaces with Adaptive AI for Intuitive Responsiveness and High-Accuracy Motor Imagery Classification—0
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Benchmark Results

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