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 7101–7150 of 15113 papers

TitleStatusHype
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms—0
Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning—0
Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling—0
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving—0
Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks—0
Reinforcement Learning Guided by Provable Normative ComplianceCode0
Marginalized Operators for Off-policy Reinforcement Learning—0
Multi-Robot Active Mapping via Neural Bipartite Graph Matching—0
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning—0
Factored Adaptation for Non-Stationary Reinforcement Learning—0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learning—0
Learning to act: a Reinforcement Learning approach to recommend the best next activities—0
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography—0
Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)—0
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning MethodCode0
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation—0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
On Reinforcement Learning, Effect Handlers, and the State Monad—0
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework—0
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information—0
5G Routing Interfered EnvironmentCode0
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study—0
Image quality assessment for machine learning tasks using meta-reinforcement learning—0
Optimizing Airborne Wind Energy with Reinforcement Learning—0
A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management—0
Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey—0
Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning—0
Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning—0
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning—0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
Dealing with Sparse Rewards Using Graph Neural Networks—0
Quasi-Newton Iteration in Deterministic Policy Gradient—0
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gaps—0
MERLIN -- Malware Evasion with Reinforcement LearnINg—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology—0
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies—0
Bellman Residual Orthogonalization for Offline Reinforcement Learning—0
An Optical Control Environment for Benchmarking Reinforcement Learning AlgorithmsCode0
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning—0
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty—0
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms—0
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games—0
X-MEN: Guaranteed XOR-Maximum Entropy Constrained Inverse Reinforcement Learning—0
Reinforcement-based frugal learning for satellite image change detection—0
Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning—0
Explainability in reinforcement learning: perspective and position—0
Linear convergence of a policy gradient method for some finite horizon continuous time control problems—0
FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics—0
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Benchmark Results

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