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 5651–5675 of 15113 papers

TitleStatusHype
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies—0
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning—0
Cross-Domain Transfer via Semantic Skill Imitation—0
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
Quantum Control based on Deep Reinforcement Learning—0
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning—0
Reinforcement Learning in System Identification—0
Robust Policy Optimization in Deep Reinforcement LearningCode0
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction—0
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning—0
Single Cell Training on Architecture Search for Image Denoising—0
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration—0
Improving generalization in reinforcement learning through forked agents—0
A Review of Off-Policy Evaluation in Reinforcement Learning—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes—0
Variance-Reduced Conservative Policy Iteration—0
Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks—0
Generalization Through the Lens of Learning Dynamics—0
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Benchmark Results

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