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 1831–1840 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning for Power System Control: A Review—0
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators—0
DEAR: Disentangled Environment and Agent Representations for Reinforcement Learning without ReconstructionCode0
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning—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
Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes—0
PUZZLES: A Benchmark for Neural Algorithmic ReasoningCode1
External Model Motivated Agents: Reinforcement Learning for Enhanced Environment SamplingCode0
Beyond Human Preferences: Exploring Reinforcement Learning Trajectory Evaluation and Improvement through LLMs—0
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Benchmark Results

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