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

Showing 79017950 of 15113 papers

TitleStatusHype
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance0
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning0
A Deep Reinforcement Learning Approach for Composing Moving IoT Services0
d3rlpy: An Offline Deep Reinforcement Learning LibraryCode0
Development of collective behavior in newborn artificial agents0
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning0
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks0
Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning0
Improving RNA Secondary Structure Design using Deep Reinforcement Learning0
An Algorithmic Theory of Metacognition in Minds and Machines0
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space0
Supervised Advantage Actor-Critic for Recommender Systems0
Successor Feature Neural Episodic Control0
Model-Free Risk-Sensitive Reinforcement Learning0
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel0
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning0
Control of a fly-mimicking flyer in complex flow using deep reinforcement learning0
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning0
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles0
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
Imagine Networks0
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies0
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework0
Autonomous Attack Mitigation for Industrial Control Systems0
AlphaD3M: Machine Learning Pipeline Synthesis0
Model-Based Episodic Memory Induces Dynamic Hybrid Controls0
Smooth Imitation Learning via Smooth Costs and Smooth Policies0
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning0
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy0
What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks0
OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach0
Integrating Pretrained Language Model for Dialogue Policy Learning0
Learning Multiresolution Matrix Factorization and its Wavelet Networks on GraphsCode0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition0
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure0
Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration0
A Generative Framework for Simultaneous Machine Translation0
Human-Level Control without Server-Grade HardwareCode0
Learning Task Sampling Policy for Multitask Learning0
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization0
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning0
Machine Learning aided Crop Yield Optimization0
Neuro-Symbolic Approaches for Text-Based Policy LearningCode0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method0
An Actor-Critic Method for Simulation-Based Optimization0
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Benchmark Results

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