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 9776–9800 of 15113 papers

TitleStatusHype
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning—0
Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning—0
Model-free Control of Chaos with Continuous Deep Q-learning—0
Model-Free Deep Reinforcement Learning in Software-Defined Networks—0
Model-Free Episodic Control with State Aggregation—0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Model-Free Imitation Learning with Policy Optimization—0
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee—0
Model-Free Learning of Safe yet Effective Controllers—0
Model-Free Linear Quadratic Control via Reduction to Expert Prediction—0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Model-Free μ Synthesis via Adversarial Reinforcement Learning—0
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning—0
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
Model-free optimal control of discrete-time systems with additive and multiplicative noises—0
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation—0
Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs—0
Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents—0
Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey—0
Model-free Reinforcement Learning for Stochastic Stackelberg Security Games—0
Model-free Reinforcement Learning for Branching Markov Decision Processes—0
Model-Free Reinforcement Learning for Symbolic Automata-encoded Objectives—0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
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Benchmark Results

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