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 5601–5625 of 15113 papers

TitleStatusHype
Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space—0
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning—0
Skill Machines: Temporal Logic Skill Composition in Reinforcement LearningCode0
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
Robust Reinforcement Learning on Graphs for Logistics optimization—0
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant RegretCode0
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation—0
Impartial Games: A Challenge for Reinforcement LearningCode0
Learning to Query Internet Text for Informing Reinforcement Learning Agents—0
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization—0
Learning in Mean Field Games: A Survey—0
Reward Uncertainty for Exploration in Preference-based Reinforcement LearningCode1
Penalized Proximal Policy Optimization for Safe Reinforcement Learning—0
Meta Policy Learning for Cold-Start Conversational RecommendationCode0
History Compression via Language Models in Reinforcement LearningCode1
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning—0
Deep Reinforcement Learning for Multi-class Imbalanced TrainingCode0
Concurrent Credit Assignment for Data-efficient Reinforcement LearningCode0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies—0
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in MachinesCode0
RL with KL penalties is better viewed as Bayesian inference—0
Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning—0
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning—0
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Benchmark Results

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