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 4251–4300 of 15113 papers

TitleStatusHype
Monitored Markov Decision ProcessesCode0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
High-Precision Geosteering via Reinforcement Learning and Particle Filters—0
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Differentially Private Deep Model-Based Reinforcement Learning—0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
Scaling Intelligent Agents in Combat Simulations for Wargaming—0
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning—0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
Convergence for Natural Policy Gradient on Infinite-State Queueing MDPs—0
Code as Reward: Empowering Reinforcement Learning with VLMs—0
Learning Diverse Policies with Soft Self-Generated Guidance—0
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs—0
Averaging n-step Returns Reduces Variance in Reinforcement Learning—0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
No-Regret Reinforcement Learning in Smooth MDPs—0
Reinforcement Learning from Bagged Reward—0
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence—0
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human ElbowsCode0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning—0
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment DesignCode0
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning—0
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences—0
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short DelaysCode0
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Abstracted Trajectory Visualization for Explainability in Reinforcement Learning—0
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control—0
The Virtues of Pessimism in Inverse Reinforcement Learning—0
Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems—0
Rethinking the Role of Proxy Rewards in Language Model AlignmentCode0
To the Max: Reinventing Reward in Reinforcement LearningCode0
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models—0
The Political Preferences of LLMs—0
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous EnvironmentsCode0
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation LearningCode0
Causal Coordinated Concurrent Reinforcement Learning—0
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Benchmark Results

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