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 5001–5050 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Plasticity Injection—0
Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness—0
A Mini Review on the utilization of Reinforcement Learning with OPC UA—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Constrained Proximal Policy Optimization—0
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry—0
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning—0
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and PracticeCode0
Offline Primal-Dual Reinforcement Learning for Linear MDPs—0
Lagrangian-based online safe reinforcement learning for state-constrained systems—0
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search—0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
BertRLFuzzer: A BERT and Reinforcement Learning Based FuzzerCode0
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning—0
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL—0
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond—0
Client Selection for Federated Policy Optimization with Environment HeterogeneityCode0
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning PoliciesCode0
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning—0
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCSCode0
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions—0
Coagent Networks: Generalized and Scaled—0
Cooperation Is All You Need—0
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality—0
Deep Reinforcement Learning-based Exploration of Web ApplicationsCode0
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes—0
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs—0
Task-Oriented Communication Design at Scale—0
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension—0
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback—0
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient AlgorithmsCode0
Towards Generalizable Reinforcement Learning for Trade Execution—0
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular NetworksCode0
Optimizing Memory Mapping Using Deep Reinforcement Learning—0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas—0
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes—0
Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning—0
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection—0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—0
RLocator: Reinforcement Learning for Bug Localization—0
Reinforcement Learning for Topic ModelsCode0
Knowledge-enhanced Agents for Interactive Text Games—0
Truncating Trajectories in Monte Carlo Reinforcement Learning—0
Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization—0
Explaining RL Decisions with TrajectoriesCode0
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications—0
Toward Evaluating Robustness of Reinforcement Learning with Adversarial PolicyCode0
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Benchmark Results

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