A Study of Model Based and Model Free Offline Reinforcement Learning
Indu Shukla, Haley R. Dozier, Althea C. Henslee · 2022
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.