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 2351–2400 of 15113 papers

TitleStatusHype
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
Constraining Action Sequences with Formal Languages for Deep Reinforcement Learning—0
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics—0
Bayesian Bellman Operators—0
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning—0
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
Constrained Upper Confidence Reinforcement Learning—0
Battery Model Calibration with Deep Reinforcement Learning—0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
BATS: Best Action Trajectory Stitching—0
An adaptive synchronization approach for weights of deep reinforcement learning—0
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning—0
Constrained Upper Confidence Reinforcement Learning with Known Dynamics—0
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning—0
Context-aware taxi dispatching at city-scale using deep reinforcement learning—0
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient—0
Batch Reinforcement Learning with Hyperparameter Gradients—0
An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems—0
Batch Reinforcement Learning Through Continuation Method—0
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences—0
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Batch Reinforcement Learning from Crowds—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation—0
Batch Policy Gradient Methods for Improving Neural Conversation Models—0
Learning "What-if" Explanations for Sequential Decision-Making—0
An Actor-Critic Method for Simulation-Based Optimization—0
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT—0
A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments—0
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Basic protocols in quantum reinforcement learning with superconducting circuits—0
Baselines for Reinforcement Learning in Text Games—0
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning—0
A Comparison of learning algorithms on the Arcade Learning Environment—0
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks—0
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems—0
A Multi-Objective Deep Reinforcement Learning Framework—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation—0
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning—0
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Benchmark Results

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