Unified AI Framework for Deep Reinforcement Learning
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Solution Overview
Problem
Current deep reinforcement learning (DRL) decision-making platforms lack compatibility with multiple AI frameworks, limiting their versatility and requiring users to adapt to specific frameworks, which hinders the development of efficient and modular decision-making systems.
Innovation Solution
A unified AI framework for DRL that includes a parameter configuration module, general-purpose module, original environment module, environment vectorization module, environments maker, mathematical utilities module, and model library, allowing for modular design and compatibility with PyTorch, TensorFlow, and MindSpore frameworks, enabling users to customize and optimize DRL models and tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a DRL platform is designed to support multiple AI frameworks, then compatibility and versatility are improved, but device complexity and integration difficulty increase
Solution Approach 1:
The platform is divided into independent framework-specific modules (PyTorch module, TensorFlow module, MindSpore module), each handling operations for its respective framework. This segmentation allows the system to support multiple frameworks without creating complex interdependencies, as each module operates independently while sharing common interfaces through the general-purpose module.
Solution Approach 2:
The general-purpose module serves as an intermediary layer between the user interface and framework-specific implementations. It provides unified parameter configuration, environment management, and algorithm implementation that are framework-agnostic, while delegating framework-specific operations to dedicated modules. This mediator pattern resolves the contradiction by abstracting away framework complexities from the core platform logic.
2Productivity
If the platform provides extensive algorithmic examples and encapsulation, then developer efficiency is improved, but ease of customization deteriorates
Solution Approach 1:
The platform implements a dynamic configuration system where users can switch between predefined algorithmic examples and custom implementations. The parameter configuration module allows dynamic adjustment of algorithm parameters, environment settings, and framework selections without requiring code changes. This dynamic approach enables users to leverage pre-built examples for quick development while maintaining full customization capability when needed.
Solution Approach 2:
The general-purpose module is designed with universal interfaces that work across all framework-specific modules and algorithmic examples. Common functions such as parameter parsing, environment management, and training loops are implemented once in the general-purpose module and automatically adapted to different frameworks and algorithms. This multi-functionality allows users to benefit from extensive pre-implemented examples while easily customizing behavior through unified parameter configurations.
Data Source
AI summary
A deep reinforcement learning (DRL) intelligent decision-making platform based on a unified AI framework includes a parameter configuration module, a general-purpose module, an original environment module, an environment vectorization module, an environments maker, a mathematical utilities module, a model library and a runner. Parameters of a DRL model are selected through the parameter configuration module, and read by the general-purpose module. Based on the read parameters, a representer, a policy module, a learner, and an intelligent agent are called from the model library and created, where necessary function definitions and optimizers are called from the mathematical utilities module. Based on the read parameters, the environment vectorization module is created based on the original environment. The intelligent agent and environments are input into the runner to compute an action output, which executes the action output to realize the intelligent decision making.