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 1641–1650 of 15113 papers

TitleStatusHype
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Diffusion Policy Policy OptimizationCode4
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planningCode0
Foundations of Multivariate Distributional Reinforcement Learning—0
Robust off-policy Reinforcement Learning via Soft Constrained Adversary—0
Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory controlCode1
Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning—0
AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN—0
On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes—0
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate ModelsCode0
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Benchmark Results

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