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 5576–5600 of 15113 papers

TitleStatusHype
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis—0
Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation—0
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022Code0
Double Deep Q Networks for Sensor Management in Space Situational Awareness—0
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal—0
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Does DQN Learn?—0
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learningCode1
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement LearningCode0
Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable ArrayCode0
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes—0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text ClassificationCode0
RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers—0
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency—0
A Fair Federated Learning Framework With Reinforcement Learning—0
Constrained Reinforcement Learning for Short Video Recommendation—0
Scalable Multi-Agent Model-Based Reinforcement LearningCode1
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function—0
Multimodal Knowledge Alignment with Reinforcement LearningCode1
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments—0
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement LearningCode2
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Benchmark Results

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