xviniette/FlappyLearning
FlappyLearning
FlappyLearning 是一个机器学习沙盒和神经网络训练器，旨在模拟神经进化策略。它提供了一个环境，其中人工代理使用遗传算法进行进化，以优化游戏模拟中的性能。
该系统采用神经进化种群模型，利用前馈神经网络来开发自主游戏代理。它通过基于世代的迭代来管理这些代理的进化，使用基于适应度的选择来确定哪些网络能够存活和繁殖。
该训练框架结合了随机权重变异以探索新行为，并结合了基于精英主义的保留机制，以在世代间保持表现最好的特征。用户可以调整种群规模和变异率等参数来微调学习过程。
Features
- Neuroevolution Frameworks - Provides a complete framework for evolving populations of neural networks across multiple generations.
- Game-Playing Agents - Develops autonomous agents that learn to master a video game environment without manual instruction.
- Weight Mutation - Introduces stochastic weight mutations to explore new behaviors and prevent the population from converging prematurely.
- Fitness-Based Selection - Evaluates agents based on game scores to determine which networks are selected for reproduction.
- Generation-Based Evolutions - Cycles through discrete training phases where each population is replaced by a refined set of descendants.
- Population-Based Training - Implements a population-based training strategy to evolve neural network weights through mutation and selection.
- Feedforward Architectures - Uses a feedforward neural network architecture to map game state inputs to binary action outputs.
- Neural Network Trainers - Implements a trainer that optimizes network weights through a population-based evolutionary loop.
- Neuroevolution Training - Trains neural networks using evolutionary algorithms to solve a game by rewarding high scores.
- Population-Based Optimization - Maintains an evolving group of candidate neural networks to optimize game performance.
- AI Game Simulations - Simulates a Flappy Bird game clone as an environment for evolving neural networks.
- Elitism Strategies - Employs elitism-based preservation to ensure the most successful neural networks are carried forward into new generations.
- Machine Learning Sandboxes - Provides a programmatic space for testing neuroevolutionary strategies and observing the learning progress of agents.
- Evolutionary Parameter Tuning - Allows tuning of population sizes and mutation rates to optimize how the model evolves over time.
