#81 JULIAN TOGELIUS, Prof. KEN STANLEY - AGI, Games, Diversity & Creativity [UNPLUGGED]

Overfitting

Reinforcement learning (RL) models often face the challenge of overfitting to specific environments, which limits their generalization capabilities. Julian Togelius explains that RL models tend to memorize environments due to their reliance on monolithic training objectives, which leads them to optimize for specific solutions at the expense of broader applicability 1. Tim Scarfe adds that the shortcut rule in RL means models achieve what they are optimized for but lose out on other potential solutions 2. This issue is compounded by the fact that RL models are often trained on small, homogeneous datasets, which further encourages overfitting.

The symbolic approach basically was a representation of the game as if you had coded it up, you know, with classes and methods and so on. So is that really intelligent?

--- Tim Scarfe

To address this, Julian Togelius suggests that generating data dynamically during training could help RL models learn more generalized skills 2.

   

Gradient Descent

Gradient descent, a dominant training paradigm in RL, has its limitations, particularly in fostering diverse and creative solutions. Julian Togelius argues that gradient descent is inherently empirical, as it is driven by data points that push the model towards specific hypotheses 3. This approach can lead to a lack of diversity in solutions, as models tend to follow the path of least resistance. Ken Stanley highlights the potential of integrating undirected mutations and quality diversity algorithms to overcome these limitations 4.

I think there's still an advantage to sort of doing the undirected mutation at various steps, like the learning algorithms of the future will almost certainly take place or sort of operate on multiple different scales.

--- Julian Togelius

By incorporating evolutionary algorithms, which allow for random hypothesis formation and testing, RL can potentially achieve more robust and creative outcomes 3.