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Found 5 papers, 2 papers with code
Towards Control-Centric Representations in Reinforcement Learning from Images
Image-based Reinforcement Learning is a practical yet challenging task.
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations
Imitation Learning (IL) aims to discover a policy by minimizing the discrepancy between the agent's behavior and expert demonstrations.
SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning
This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods.
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control
Trust region methods and maximum entropy methods are two state-of-the-art branches used in reinforcement learning (RL) for the benefits of stability and exploration in continuous environments, respectively.
GANE: A Generative Adversarial Network Embedding
Network embedding has become a hot research topic recently which can provide low-dimensional feature representations for many machine learning applications.
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