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 8351–8400 of 15113 papers

TitleStatusHype
Network Defense is Not a Game—0
Prospective Artificial Intelligence Approaches for Active Cyber Defence—0
Visual Navigation with Spatial AttentionCode1
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model—0
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting—0
Deep Reinforcement Learning in a Monetary Model—0
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems—0
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior—0
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls—0
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement LearningCode0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Constraints Satisfiability Driven Reinforcement Learning for Autonomous Cyber Defense—0
Approximated Multi-Agent Fitted Q Iteration—0
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement LearningCode1
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning—0
Reinforcement learning based process optimization and strategy development in conventional tunnelingCode0
Action Advising with Advice Imitation in Deep Reinforcement LearningCode0
Learning on a Budget via Teacher ImitationCode0
Language Models are Few-Shot ButlersCode1
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
Towards Standardising Reinforcement Learning Approaches for Production Scheduling ProblemsCode1
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale—0
Predictor-Corrector(PC) Temporal Difference(TD) Learning (PCTD)—0
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills—0
An L^2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation—0
Generalising Discrete Action Spaces with Conditional Action TreesCode1
Discover the Hidden Attack Path in Multi-domain Cyberspace Based on Reinforcement Learning—0
Joint Attention for Multi-Agent Coordination and Social Learning—0
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing—0
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction—0
Quantum Architecture Search via Deep Reinforcement LearningCode1
GridToPix: Training Embodied Agents with Minimal Supervision—0
A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-GoalsCode0
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback—0
Safe Continuous Control with Constrained Model-Based Policy OptimizationCode0
Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images—0
Reinforcement learning for Admission Control in 5G Wireless Networks—0
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report—0
Podracer architectures for scalable Reinforcement LearningCode1
Reward Shaping with Subgoals for Social Navigation—0
Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning—0
Online and Offline Reinforcement Learning by Planning with a Learned ModelCode1
Data-Driven Reinforcement Learning for Virtual Character Animation Control—0
Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devices—0
Reward Shaping with Dynamic Trajectory Aggregation—0
Two-stage training algorithm for AI robot soccer—0
Survey on reinforcement learning for language processing—0
Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning—0
A coevolutionary approach to deep multi-agent reinforcement learningCode1
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Benchmark Results

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