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 7726–7750 of 15113 papers

TitleStatusHype
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
Model-Free Reinforcement Learning for Asset Allocation—0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation—0
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications—0
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization—0
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity—0
Model-free Representation Learning and Exploration in Low-rank MDPs—0
Model-Free Risk-Sensitive Reinforcement Learning—0
Model-Free RL Agents Demonstrate System 1-Like Intentionality—0
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees—0
Model-Free Unsupervised Learning for Optimization Problems with Constraints—0
Model Generation with Provable Coverability for Offline Reinforcement Learning—0
Model Imitation for Model-Based Reinforcement Learning—0
Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach—0
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning—0
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Benchmark Results

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