Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow

• URL: http://arxiv.org/abs/2103.14407v1
• Date: Fri, 26 Mar 2021 11:32:27 GMT
• Title: Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
• Authors: John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried
• Abstract summary: Bellman aims to fill this gap and introduces the first thoroughly designed and tested model-based RL toolbox. Our modular approach enables to combine a wide range of environment models with generic model-based agent classes that recover state-of-the-art algorithms.
• Score: 14.422129911404472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-efficient than model-free methods in terms of agent-environment interactions, because the model enables to extrapolate to unseen situations. In the more recent past, model-based methods have shown superior results compared to model-free methods in some challenging domains with non-linear state transitions. At the same time, it has become apparent that RL is not market-ready yet and that many real-world applications are going to require model-based approaches, because model-free methods are too sample-inefficient and show poor performance in early stages of training. The latter is particularly important in industry, e.g. in production systems that directly impact a company's revenue. This demonstrates the necessity for a toolbox to push the boundaries for model-based RL. While there is a plethora of toolboxes for model-free RL, model-based RL has received little attention in terms of toolbox development. Bellman aims to fill this gap and introduces the first thoroughly designed and tested model-based RL toolbox using state-of-the-art software engineering practices. Our modular approach enables to combine a wide range of environment models with generic model-based agent classes that recover state-of-the-art algorithms. We also provide an experiment harness to compare both model-free and model-based agents in a systematic fashion w.r.t. user-defined evaluation metrics (e.g. cumulative reward). This paves the way for new research directions, e.g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.

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