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 3726–3750 of 15113 papers

TitleStatusHype
Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking ControlCode0
Self-supervised network distillation: an effective approach to exploration in sparse reward environmentsCode0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
Reinforcement Learning for Block Decomposition of CAD Models—0
Assessment of Reinforcement Learning for Macro PlacementCode2
A Reinforcement Learning Framework for Online Speaker Diarization—0
Adversarial Model for Offline Reinforcement Learning—0
Robust Auto-landing Control of an agile Regional Jet Using Fuzzy Q-learning—0
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management—0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Towards a Sustainable Internet-of-Underwater-Things based on AUVs, SWIPT, and Reinforcement Learning—0
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement LearningCode0
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
Minimax-Bayes Reinforcement LearningCode0
Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow—0
Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning—0
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space—0
Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs—0
UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Multiagent Inverse Reinforcement Learning via Theory of Mind ReasoningCode0
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue SystemsCode0
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Benchmark Results

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