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 10101–10125 of 15113 papers

TitleStatusHype
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
Motion Prediction on Self-driving Cars: A Review—0
Sample-efficient Reinforcement Learning in Robotic Table Tennis—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
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping—0
LBGP: Learning Based Goal Planning for Autonomous Following in Front—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks—0
Generative Inverse Deep Reinforcement Learning for Online Recommendation—0
XCSF for Automatic Test Case PrioritizationCode0
Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods—0
Online Observer-Based Inverse Reinforcement Learning—0
Control with adaptive Q-learningCode0
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time—0
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning—0
Incorporating Rivalry in Reinforcement Learning for a Competitive Game—0
Causal Campbell-Goodhart's law and Reinforcement LearningCode0
Cooperative Heterogeneous Deep Reinforcement Learning—0
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting—0
Exact Asymptotics for Linear Quadratic Adaptive ControlCode0
Fast Reinforcement Learning with Incremental Gaussian Mixture Models—0
Depth Self-Optimized Learning Toward Data ScienceCode0
Information-theoretic Task Selection for Meta-Reinforcement Learning—0
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Benchmark Results

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