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 6826–6850 of 15113 papers

TitleStatusHype
RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers—0
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text ClassificationCode0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable ArrayCode0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes—0
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning—0
Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space—0
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
Robust Reinforcement Learning on Graphs for Logistics optimization—0
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments—0
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant RegretCode0
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function—0
Skill Machines: Temporal Logic Skill Composition in Reinforcement LearningCode0
Learning in Mean Field Games: A Survey—0
Impartial Games: A Challenge for Reinforcement LearningCode0
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization—0
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation—0
Learning to Query Internet Text for Informing Reinforcement Learning Agents—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning—0
Concurrent Credit Assignment for Data-efficient Reinforcement LearningCode0
Deep Reinforcement Learning for Multi-class Imbalanced TrainingCode0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Meta Policy Learning for Cold-Start Conversational RecommendationCode0
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Benchmark Results

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