[Paper Review] Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning

Linsen Dong, Guanyu Gao+3 more|arXiv (Cornell University)|Apr 23, 2019

Reinforcement Learning in Robotics4 citations

TL;DR

Baconian is a unified, open-source framework designed to streamline Model-Based Reinforcement Learning (MBRL) research by providing modular, reusable components for implementing and benchmarking state-of-the-art MBRL algorithms. It reduces implementation overhead through configurable, plug-and-play modules for environments, algorithms, training control flows, and experiment monitoring, enabling rapid prototyping and reproducible experiments with minimal code.

ABSTRACT

Model-Based Reinforcement Learning (MBRL) is one category of Reinforcement Learning (RL) algorithms which can improve sampling efficiency by modeling and approximating system dynamics. It has been widely adopted in the research of robotics, autonomous driving, etc. Despite its popularity, there still lacks some sophisticated and reusable open-source frameworks to facilitate MBRL research and experiments. To fill this gap, we develop a flexible and modularized framework, Baconian, which allows researchers to easily implement a MBRL testbed by customizing or building upon our provided modules and algorithms. Our framework can free users from re-implementing popular MBRL algorithms from scratch thus greatly save users' efforts on MBRL experiments.

Motivation & Objective

• Address the lack of a unified, extensible open-source framework for Model-Based Reinforcement Learning (MBRL) research.
• Reduce the effort required to implement, reproduce, and benchmark MBRL algorithms by providing reusable, modular components.
• Support both model-based and model-free RL algorithms to enhance flexibility and broaden applicability.
• Facilitate rapid prototyping of new MBRL algorithms through plug-and-play design and minimal code configuration.
• Provide comprehensive experiment utilities, including hyperparameter management, logging, and visualization, to streamline research workflows.

Proposed method

• Design a modular framework with three core components: Experiment Manager, Training Engine, and Monitor to decouple complex dependencies.
• Implement a Control Flow module within the Training Engine to abstract and orchestrate the training process, including data collection, policy and dynamics model optimization, and testing.
• Support integration with TensorFlow for neural network building, training, and management to enable scalable model learning.
• Provide configurable, replaceable modules for environments (e.g., OpenAI Gym, DeepMind Control Suite), algorithms (e.g., ME-TRPO, iLQR, MPC, PPO), and exploration strategies.
• Integrate a user-friendly hyperparameter management system to simplify configuration, loading, and saving across experiments.
• Enable experiment reproducibility and diagnostics through standardized logging, status tracking, and result recording via the Monitor and Experiment Recorder components.

Experimental results

Research questions

• RQ1 How can a unified open-source framework reduce the implementation burden for researchers working on Model-Based Reinforcement Learning?
• RQ2 To what extent can a modular framework support both model-based and model-free RL algorithms within a single, extensible system?
• RQ3 Can a standardized control flow abstraction simplify the orchestration of complex MBRL training pipelines?
• RQ4 How effectively can such a framework support rapid prototyping and benchmarking of new MBRL algorithms with minimal code changes?
• RQ5 What role does integrated experiment management (e.g., logging, hyperparameter handling) play in improving reproducibility and efficiency in MBRL research?

Key findings

• Baconian successfully reduces the effort required to implement and reproduce MBRL algorithms by providing pre-implemented, modular components for environments, algorithms, and training control flows.
• The framework supports a wide range of state-of-the-art MBRL algorithms, including Dyna, ME-TRPO, MPC, iLQR, DQN, DDPG, and PPO, enabling direct comparison and benchmarking.
• Integrated experiment utilities, such as hyperparameter management and logging, significantly reduce the overhead of experiment setup and configuration.
• The modular architecture allows users to customize or directly use built-in modules, enabling rapid prototyping with minimal code, as demonstrated by the provided step-by-step configuration guide.
• The framework is extensible and reusable, with detailed documentation and API references available at https://baconian-public.readthedocs.io, supporting adoption by the broader research community.
• Preliminary benchmark results are included in the codebase, demonstrating the framework’s readiness for immediate use in research and experimentation.