本文探讨了模型驱动方法在控制理论领域的应用，强调了环境假设与近似的重要性。通过监督学习，如游戏训练世界模型，介绍了几种关键方法：世界模型利用变分自编码器实现梦境学习；Imagination-Augmented Agents结合预测与计划；Model-Based Priors for Model-Free Reinforcement Learning融合了有模型和无模型学习；Model-Based Value Expansion改善了价值估计，减少了学习复杂度。
Model based methods can be used in Control Theory. Environment has assumptions and approximations.
- Learn the model. By supervised learning, for instance. Play the game then train the world model.
- World models: one of my favorite approaches in which the agent can learn from it’s own “dreams” due to the Variable Auto-encoders, See paper and code.
- Imagination-Augmented Agents (I2A): learns to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. BAsically it’s a hybrid learning method because it combines model-baes and model-free methods. Paper and implementation.
- Model-Based Priors for Model-Free Reinforcement Learning (MBMF): aims to bridge tge gap between model-free and model-based reinforcement learning. See paper and code.
- Model-Based Value Expansion (MBVE): Authors of the paper state that this method controls for uncertainty in the model by only allowing imagination to fixed depth. By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, we improve value estimation, which, in turn, reduces the sample complexity of learning.
- Learn given the model
- Check alphaGo-zero
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