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 7926–7950 of 15113 papers

TitleStatusHype
AlphaD3M: Machine Learning Pipeline Synthesis—0
Model-Based Episodic Memory Induces Dynamic Hybrid Controls—0
Smooth Imitation Learning via Smooth Costs and Smooth Policies—0
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning—0
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy—0
What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks—0
OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Integrating Pretrained Language Model for Dialogue Policy Learning—0
Learning Multiresolution Matrix Factorization and its Wavelet Networks on GraphsCode0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration—0
A Generative Framework for Simultaneous Machine Translation—0
Human-Level Control without Server-Grade HardwareCode0
Learning Task Sampling Policy for Multitask Learning—0
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization—0
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning—0
Machine Learning aided Crop Yield Optimization—0
Neuro-Symbolic Approaches for Text-Based Policy LearningCode0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
An Actor-Critic Method for Simulation-Based Optimization—0
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Benchmark Results

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