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 3291–3300 of 15113 papers

TitleStatusHype
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems—0
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations—0
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning—0
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning—0
A Succinct Summary of Reinforcement Learning—0
Deep Reinforcement Learning for NLP—0
A Gentle Lecture Note on Filtrations in Reinforcement Learning—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems—0
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Benchmark Results

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