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 2931–2940 of 15113 papers

TitleStatusHype
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules—0
Constrained Reinforcement Learning Has Zero Duality Gap—0
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection—0
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
A Tractable Algorithm For Finite-Horizon Continuous Reinforcement Learning—0
A Hybrid PAC Reinforcement Learning Algorithm—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
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Benchmark Results

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