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

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Showing 6451–6500 of 15113 papers

TitleStatusHype
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning—0
Cooperative Actor-Critic via TD Error Aggregation—0
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks—0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Online Reinforcement Learning for Periodic MDP—0
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations—0
Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement LearningCode0
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System—0
Halftoning with Multi-Agent Deep Reinforcement Learning—0
Epersist: A Self Balancing Robot Using PID Controller And Deep Reinforcement Learning—0
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution—0
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement LearningCode0
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning—0
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search—0
Strategising template-guided needle placement for MR-targeted prostate biopsy—0
Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning—0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning—0
On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RANCode0
Successor Representation Active InferenceCode0
Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks—0
Riemannian Stochastic Gradient Method for Nested Composition Optimization—0
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement LearningCode0
New Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning—0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios—0
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments—0
Few-Shot Teamwork—0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
Actor-Critic based Improper Reinforcement Learning—0
Boolean Decision Rules for Reinforcement Learning Policy Summarisation—0
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
MLGOPerf: An ML Guided Inliner to Optimize Performance—0
MAD for Robust Reinforcement Learning in Machine Translation—0
A framework for online, stabilizing reinforcement learning—0
Minimum Description Length Control—0
Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning—0
Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients—0
Context sequence theory: a common explanation for multiple types of learning—0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—0
Associative Memory Based Experience Replay for Deep Reinforcement Learning—0
BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion—0
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement LearningCode0
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions—0
Optimizing Data Collection in Deep Reinforcement Learning—0
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space—0
Skill-based Model-based Reinforcement Learning—0
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning—0
Multi-Agent Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station—0
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Benchmark Results

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