Python code for the Implementation of DEvelopmentAl Learning (IDEAL) Course

The course is composed of 7 lessons:

1. The lesson builds a simple agent that memorizes interactions and uses them to predict the results when repeating experiments;
2. The lesson extends the agent attaching a valence to each interaction and uses these valences to avoid negative interactions;
3. The lesson extends the agent to learn composite interactions and uses them to exploit regularities in the sequences of interactions;
4. The lesson extends the agent with self-programming using abstract experiments and uses them to exploit longer sequences of interactions;
5. The lesson simplifies the agent with recursive learning and achieves complex behavior in a maze environment;
6. The lesson contains discussion and demonstrations about cognitive architectures;
7. The lesson contains discussion about research into developmental artificial intelligence.

The source files implement the agents for each lesson.

The trace files are the outputs generated by running the agents.