1.
Meta-Learning
1.1.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
1.2.
HOW TO TRAIN YOUR MAML
1.3.
RAPID LEARNING OR FEATURE REUSE? TOWARDS UNDERSTANDING THE EFFECTIVENESS OF MAML
1.4.
On First-Order Meta-Learning Algorithms
1.5.
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
1.6.
Meta-Learning with Implicit Gradients
2.
Meta-RL
2.1.
Meta-Reinforcement Learning of Structured Exploration Strategies
2.2.
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
3.
Autonomous Systems
3.1.
[review]Planning and Decision-Making for Autonomous Vehicles
3.2.
[review]A Review of Mobile Robot Motion Planning Methods
3.3.
RL
3.3.1.
Asynchronous Methods for Deep Reinforcement Learning (A3C)
3.3.2.
LEARNING TO NAVIGATE IN COMPLEX ENVIRONMENTS
3.3.3.
Curiosity-driven Exploration by Self-supervised Prediction
3.3.4.
End-to-End Navigation Strategy With Deep Reinforcement Learning for Mobile Robots
3.3.5.
LEARNING TO NAVIGATE IN COMPLEX ENVIRONMENTS
3.3.6.
Learning to Navigate Through Complex Dynamic Environment With Modular Deep Reinforcement Learning
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