Gwenael Edouard Herve Bocquet

1.1 Introduction

1.2 Weight configurations of sensor-motor connections in Braitenberg vehicles

1.3 Solution of weights and connections

1.4 Biological and non-biological bodies

1.5 Sensory input and motor output

1.7 Learning and synaptic plasticity

1.8 Introducing a formal framework for representing learning

1.6 Different types of learning

3.1 Theory of reinforcement learning, Introductory question

3.2 A robotics example

3.3 Theory of reinforcement learning, Grid examples

3.4 Exploration and exploitation

3.6 Eligibility traces

4.1 Introduction to policy gradient

4.2 How to change the policy with a gradient method

4.3 Policy estimation

4.6 Relationship to neural learning rules

Recap, Functions and Pattern Matching

Recap, Collections

Asynchronous Programming

Asynchronous Programming with Future

Introduction, Why Actors

The Actor Model

Designing Actor Systems

Message Processing Semantics

Testing Actor Systems

Failure Handling with Actors

Lifecycle Monitoring and The Error Kernel

Persistent Actor State

Actor Composition