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 2401–2450 of 15113 papers

TitleStatusHype
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated PoliciesCode0
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
Offline Multi-task Transfer RL with Representational Penalization—0
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Self-evolving Autoencoder Embedded Q-Network—0
Programmatic Reinforcement Learning: Navigating Gridworlds—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Modelling crypto markets by multi-agent reinforcement learningCode0
Policy Learning for Off-Dynamics RL with Deficient SupportCode1
Jack of All Trades, Master of Some, a Multi-Purpose Transformer AgentCode2
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation—0
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference AdjustmentCode1
Performative Reinforcement Learning in Gradually Shifting EnvironmentsCode0
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption—0
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories—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
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea—0
Hybrid Inverse Reinforcement LearningCode1
Intelligent Agricultural Management Considering N_2O Emission and Climate Variability with Uncertainties—0
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints—0
Auxiliary Reward Generation with Transition Distance Representation Learning—0
IR-Aware ECO Timing Optimization Using Reinforcement Learning—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
Natural Language Reinforcement Learning—0
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments—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
Monitored Markov Decision ProcessesCode0
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
High-Precision Geosteering via Reinforcement Learning and Particle Filters—0
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
Scaling Intelligent Agents in Combat Simulations for Wargaming—0
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
Differentially Private Deep Model-Based Reinforcement Learning—0
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement LearningCode2
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
QGFN: Controllable Greediness with Action ValuesCode1
Convergence for Natural Policy Gradient on Infinite-State Queueing MDPs—0
Safety Filters for Black-Box Dynamical Systems by Learning Discriminating HyperplanesCode1
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
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Benchmark Results

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