2017/09/26 by Andreas Kirsch, Kirsch, Andreas · 2 citations
Computer Science · #Reinforcement Learning in Robotics #Distributed and Parallel Computing Systems #Evolutionary Algorithms and Applications
paper · pdf · doi:10.48550/arxiv.1709.09069
The OpenAI Gym provides researchers and enthusiasts with simple to use environments for reinforcement learning. Even the simplest environment have a level of complexity that can obfuscate the inner workings of RL approaches and make debugging difficult. This whitepaper describes a Python framework that makes it very easy to create simple Markov-Decision-Process environments programmatically by specifying state transitions and rewards of deterministic and non-deterministic MDPs in a domain-specific language in Python. It then presents results and visualizations created with this MDP framework.