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 5901–5925 of 15113 papers

TitleStatusHype
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning—0
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine LearningCode0
Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting—0
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs—0
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning—0
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation—0
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks—0
Safe Reinforcement Learning via Shielding under Partial Observability—0
Automating Staged Rollout with Reinforcement Learning—0
A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control SystemsCode0
Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid ControlCode0
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation—0
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation—0
Building Decision Forest via Deep Reinforcement Learning—0
Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture—0
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms—0
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools—0
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving—0
Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning—0
Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling—0
Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks—0
Factored Adaptation for Non-Stationary Reinforcement Learning—0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
Reinforcement Learning Guided by Provable Normative ComplianceCode0
Multi-Robot Active Mapping via Neural Bipartite Graph Matching—0
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Benchmark Results

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