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 7701–7725 of 15113 papers

TitleStatusHype
Graph Pruning for Model Compression—0
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing—0
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic—0
Designing Heterogeneous GNNs with Desired Permutation Properties for Wireless Resource Allocation—0
Large-Scale Graph Reinforcement Learning in Wireless Control Systems—0
Graph Signal Sampling via Reinforcement Learning—0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
Graph Value Iteration—0
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning—0
GrASP: Gradient-Based Affordance Selection for Planning—0
Graying the black box: Understanding DQNs—0
Greedy Bandits with Sampled Context—0
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity—0
Greedy-Step Off-Policy Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge—0
Griddly: A platform for AI research in games—0
GriddlyJS: A Web IDE for Reinforcement Learning—0
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search—0
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management—0
GridToPix: Training Embodied Agents with Minimal Supervision—0
GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning—0
GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal—0
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Benchmark Results

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