Variational Markov Decision Processes
A TensorFlow 2 implementation of Variational Markov Decision Processes, a framework allowing to (i) distill policies learned through (deep) reinforcement learning and (ii) learn discrete abstractions of continuous environments, the two with bisimulation guarantees.
The source code provided allows replicating the experiments presented in the paper Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes.
- Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes
- Simple Strategies in Multi-Objective MDPs
- Life is Random, Time is Not: Markov Decision Processes with Window Objectives
- Safe Reinforcement Learning
- A Framework for Flexibly Guiding Learning Agents