Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning

Understand

We present a new deep meta reinforcement learner, which we call Deep Episodic Value Iteration (DEVI).

• DEVI uses a deep neural network to learn a similarity metric for a non-parametric model-based reinforcement learning algorithm.
• Our model is trained end-to-end via back-propagation.
• Despite being trained using the model-free Q-learning objective, we show that DEVI's model-based internal structure provides `one-shot' transfer to changes in reward and transition structure, even for tasks with very high-dimensional state spaces.