Differentiable Tree Search Network

Understand

In decision-making problems with limited training data, policy functions approximated using deep neural networks often exhibit suboptimal performance.

• An alternative approach involves learning a world model from the limited data and determining actions through online search.
• However, the performance is adversely affected by compounding errors arising from inaccuracies in the learned world model.
• While methods like TreeQN have attempted to address these inaccuracies by incorporating algorithmic inductive biases into the neural network architectures, the biases they introduce are often weak and insufficient for complex decision-making tasks.