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 9501–9525 of 15113 papers

TitleStatusHype
On the role of planning in model-based deep reinforcement learning—0
Reliable Off-policy Evaluation for Reinforcement Learning—0
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient—0
Exploring market power using deep reinforcement learning for intelligent bidding strategies—0
Drafting in Collectible Card Games via Reinforcement LearningCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Universal Activation Function For Machine Learning—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
Sample-efficient Reinforcement Learning in Robotic Table Tennis—0
Motion Prediction on Self-driving Cars: A Review—0
The Value Equivalence Principle for Model-Based Reinforcement Learning—0
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments—0
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement LearningCode1
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
LBGP: Learning Based Goal Planning for Autonomous Following in Front—0
Learning a Decentralized Multi-arm Motion PlannerCode1
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping—0
XCSF for Automatic Test Case PrioritizationCode0
Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods—0
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement LearningCode1
Generative Inverse Deep Reinforcement Learning for Online Recommendation—0
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks—0
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning—0
Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time—0
Control with adaptive Q-learningCode0
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Benchmark Results

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