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 4161–4170 of 15113 papers

TitleStatusHype
Single Cell Training on Architecture Search for Image Denoising—0
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration—0
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning—0
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
Variance-Reduced Conservative Policy Iteration—0
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes—0
MoDem: Accelerating Visual Model-Based Reinforcement Learning with DemonstrationsCode1
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
Reinforcement Learning and Tree Search Methods for the Unit Commitment ProblemCode1
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
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Benchmark Results

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