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 3751–3760 of 15113 papers

TitleStatusHype
DEAR: Disentangled Environment and Agent Representations for Reinforcement Learning without ReconstructionCode0
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators—0
Safe Reinforcement Learning for Power System Control: A Review—0
Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes—0
Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks—0
A Review of Safe Reinforcement Learning Methods for Modern Power Systems—0
Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning—0
Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems—0
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels—0
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control PriorsCode0
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Benchmark Results

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