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 4676–4700 of 15113 papers

TitleStatusHype
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap—0
An Extensible Interactive Interface for Agent Design—0
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning—0
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
An Improved Reinforcement Learning Model Based on Sentiment Analysis—0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
An Independent Study of Reinforcement Learning and Autonomous Driving—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
An Inductive Synthesis Framework for Verifiable Reinforcement Learning—0
An Information-Theoretic Analysis of Bayesian Reinforcement Learning—0
An Information Theoretic Approach to Interaction-Grounded Learning—0
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning—0
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning—0
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey—0
An initial attempt of combining visual selective attention with deep reinforcement learning—0
An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm—0
An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning—0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
An Intelligent Energy Management Framework for Hybrid-Electric Propulsion Systems Using Deep Reinforcement Learning—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
An Introduction to Quantum Reinforcement Learning (QRL)—0
Introduction to Reinforcement Learning—0
An introduction to reinforcement learning for neuroscience—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
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Benchmark Results

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