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 5151–5175 of 15113 papers

TitleStatusHype
On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RANCode0
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge DesignCode1
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search—0
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement LearningCode0
Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning—0
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning—0
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book ModelCode1
Discriminator-Weighted Offline Imitation Learning from Suboptimal DemonstrationsCode1
Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks—0
Successor Representation Active InferenceCode0
New Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning—0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios—0
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement LearningCode0
Riemannian Stochastic Gradient Method for Nested Composition Optimization—0
Bayesian Generational Population-Based TrainingCode1
Few-Shot Teamwork—0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments—0
Actor-Critic based Improper Reinforcement Learning—0
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal ReasoningCode1
An Information-Theoretic Analysis of Bayesian Reinforcement Learning—0
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation—0
MAD for Robust Reinforcement Learning in Machine Translation—0
MLGOPerf: An ML Guided Inliner to Optimize Performance—0
A framework for online, stabilizing reinforcement learning—0
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Benchmark Results

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