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 4151–4175 of 15113 papers

TitleStatusHype
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction—0
Robust Policy Optimization in Deep Reinforcement LearningCode0
Quantum Control based on Deep Reinforcement Learning—0
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand SystemsCode1
Cross-Domain Transfer via Semantic Skill Imitation—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario—0
Improving generalization in reinforcement learning through forked agents—0
A Review of Off-Policy Evaluation in Reinforcement Learning—0
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
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization—0
Generalization Through the Lens of Learning Dynamics—0
Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks—0
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Benchmark Results

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