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 6801–6825 of 15113 papers

TitleStatusHype
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Designing Rewards for Fast Learning—0
Efficient Reward Poisoning Attacks on Online Deep Reinforcement LearningCode0
Learning Open Domain Multi-hop Search Using Reinforcement Learning—0
A Simulation Environment and Reinforcement Learning Method for Waste Reduction—0
Learning Security Strategies through Game Play and Optimal Stopping—0
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning—0
Survival Analysis on Structured Data using Deep Reinforcement Learning—0
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning—0
Non-Markovian policies occupancy measures—0
Off-Beat Multi-Agent Reinforcement Learning—0
Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks—0
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters—0
Provably Sample-Efficient RL with Side Information about Latent Dynamics—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022Code0
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal—0
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis—0
Double Deep Q Networks for Sensor Management in Space Situational Awareness—0
Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation—0
Does DQN Learn?—0
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement LearningCode0
Constrained Reinforcement Learning for Short Video Recommendation—0
A Fair Federated Learning Framework With Reinforcement Learning—0
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency—0
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Benchmark Results

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