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 2351–2375 of 15113 papers

TitleStatusHype
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
Constrained Attractor Selection Using Deep Reinforcement Learning—0
Constrained Combinatorial Optimization with 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
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games—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
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
Constrained Cross-Entropy Method for Safe Reinforcement Learning—0
Constrained Reinforcement Learning for Short Video Recommendation—0
Constrained Text Generation with Global Guidance -- Case Study on CommonGen—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
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Benchmark Results

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