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 7176–7200 of 15113 papers

TitleStatusHype
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Automating Privilege Escalation with Deep Reinforcement Learning—0
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning—0
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming EventsCode0
Decentralized Safe Reinforcement Learning for Voltage Control—0
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances—0
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations—0
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement LearningCode0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement LearningCode0
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning—0
Offline Reinforcement Learning with Reverse Model-based ImaginationCode1
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems—0
Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning—0
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning—0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Guiding Evolutionary Strategies by Differentiable Robot SimulatorsCode0
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Neural Network Verification in Control—0
MOLUCINATE: A Generative Model for Molecules in 3D SpaceCode1
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces—0
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Benchmark Results

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