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

Showing 63516400 of 15113 papers

TitleStatusHype
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles0
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity0
Derivative-Free Reinforcement Learning: A Review0
Description Based Text Classification with Reinforcement Learning0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes0
Design and Development of Spoken Dialogue System in Indic Languages0
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems0
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning0
Design for a Darwinian Brain: Part 2. Cognitive Architecture0
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization0
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning0
Designing Deep Reinforcement Learning for Human Parameter Exploration0
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning0
Designing Interpretable Approximations to Deep Reinforcement Learning0
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach0
Designing realistic RL environment for power systems0
Designing Rewards for Fast Learning0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning0
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter0
Design of Interacting Particle Systems for Fast Linear Quadratic RL0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency0
Design Principles of the Hippocampal Cognitive Map0
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention0
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning0
Detecting and Adapting to Novelty in Games0
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study0
Detecting Deceptive Reviews using Generative Adversarial Networks0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning0
Deterministic Exploration via Stationary Bellman Error Maximization0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning0
Deterministic Value-Policy Gradients0
DETERRENT: Detecting Trojans using Reinforcement Learning0
Developing cooperative policies for multi-stage tasks0
Developing cooperative policies for multi-stage reinforcement learning tasks0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning0
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents0
Development of collective behavior in newborn artificial agents0
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance0
Dexterous Manipulation through Imitation Learning: A Survey0
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost0
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management0
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Benchmark Results

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