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 7126–7150 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 286 of 605Next →

Benchmark Results

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