A Study of Model Based and Model Free Offline Reinforcement Learning

Reinforcement learning (RL) has received considerable attention for building autonomous systems with trained agents that interact with environments to learn optimal behavior. The RL requires a fundamentally online learning paradigm, one of the biggest obstacles for the widespread adoption of RL in scaling to many real-world scenarios. In this study we have explored the method of data collection and have applied offline model-free and model-based RL methods on classical OpenAIGym Cart-Pole environment. In this approach offline algorithms find a good policy from previously collected dataset.