Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method

Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by performing actions that interact with the environment. The relational representation allows more dynamic environment states than an attribute-based representation of reinforcement learning, but this flexibility also creates new problems such as a potentially infinite number of states. This thesis describes an RRL algorithm named Cerrla that creates policies directly from a set of learned relational “condition-action” rules using the Cross-Entropy Method (CEM) to control policy creation. The CEM assigns each rule a sampling probability and gradually modifies these probabilities such that the randomly sampled policies consist of ‘better’ rules, resulting in larger rewards received. Rule creation is guided by an inferred partial model of the environment that defines: the minimal conditions needed to take an action, the possible specialisation conditions per rule, and a set of simplification rules to remove redundant and illegal rule conditions, resulting in compact, efficient, and comprehensible policies. Cerrla is evaluated on four separate environments, where each environment has several different goals. Results show that compared to existing RRL algorithms, Cerrla is able to learn equal or better behaviour in less time on the standard RRL environment. On other larger, more complex environments, it can learn behaviour that is competitive to specialised approaches. The simplified rules and CEM’s bias towards compact policies result in comprehensive and effective relational policies created in a relatively short amount of time

my Human Brain Project (mHBP)

How can we make an agent that thinks like us humans? An agent that can have proprioception, intrinsic motivation, identify deception, use small amounts of energy, transfer knowledge between tasks and evolve? This is the problem that this thesis is focusing on. Being able to create a piece of software that can perform tasks like a human being, is a goal that, if achieved, will allow us to extend our own capabilities to a very high level, and have more tasks performed in a predictable fashion. This is one of the motivations for this thesis. To address this problem, we have proposed a modular architecture for Reinforcement Learning computation and developed an implementation to have this architecture exercised. This software, that we call mHBP, is created in Python using Webots as an environment for the agent, and Neo4J, a graph database, as memory. mHBP takes the sensory data or other inputs, and produces, based on the body parts / tools that the agent has available, an output consisting of actions to perform. This thesis involves experimental design with several iterations, exploring a theoretical approach to RL based on graph databases. We conclude, with our work in this thesis, that it is possible to represent episodic data in a graph, and is also possible to interconnect Webots, Python and Neo4J to support a stable architecture for Reinforcement Learning. In this work we also find a way to search for policies using the Neo4J querying language: Cypher. Another key conclusion of this work is that state representation needs to have further research to find a state definition that enables policy search to produce more useful policies. The article “REINFORCEMENT LEARNING: A LITERATURE REVIEW (2020)” at Research Gate with doi 10.13140/RG.2.2.30323.76327 is an outcome of this thesis.Como podemos criar um agente que pense como nós humanos? Um agente que tenha propriocepção, motivação intrínseca, seja capaz de identificar ilusão, usar pequenas quantidades de energia, transferir conhecimento entre tarefas e evoluir? Este é o problema em que se foca esta tese. Ser capaz de criar uma peça de software que desempenhe tarefas como um ser humano é um objectivo que, se conseguido, nos permitirá estender as nossas capacidades a um nível muito alto, e conseguir realizar mais tarefas de uma forma previsível. Esta é uma das motivações desta tese. Para endereçar este problema, propomos uma arquitectura modular para computação de aprendizagem por reforço e desenvolvemos uma implementação para exercitar esta arquitetura. Este software, ao qual chamamos mHBP, foi criado em Python usando o Webots como um ambiente para o agente, e o Neo4J, uma base de dados de grafos, como memória. O mHBP recebe dados sensoriais ou outros inputs, e produz, baseado nas partes do corpo / ferramentas que o agente tem disponíveis, um output que consiste em ações a desempenhar. Uma boa parte desta tese envolve desenho experimental com diversas iterações, explorando uma abordagem teórica assente em bases de dados de grafos. Concluímos, com o trabalho nesta tese, que é possível representar episódios em um grafo, e que é, também, possível interligar o Webots, com o Python e o Neo4J para suportar uma arquitetura estável para a aprendizagem por reforço. Neste trabalho, também, encontramos uma forma de procurar políticas usando a linguagem de pesquisa do Neo4J: Cypher. Outra conclusão chave deste trabalho é que a representação de estados necessita de mais investigação para encontrar uma definição de estado que permita à pesquisa de políticas produzir políticas que sejam mais úteis. O artigo “REINFORCEMENT LEARNING: A LITERATURE REVIEW (2020)” no Research Gate com o doi 10.13140/RG.2.2.30323.76327 é um sub-produto desta tese

Policy Gradient Critics

We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov Decision Processes (POMDPs) that require long-term memories of past observations and actions. The approach involves estimating a policy gradient for an Actor through a Policy Gradient Critic which evaluates probability distributions on actions. Gradient-based updates of history-conditional action probability distributions enable the algorithm to learn a mapping from memory states (or event histories) to probability distributions on actions, solving POMDPs through a combination of memory and stochasticity. This goes beyond previous approaches to learning purely reactive POMDP policies, without giving up their advantages. Preliminary results on important benchmark tasks show that our approach can in principle be used as a general purpose POMDP algorithm that solves RL problems in both continuous and discrete action domains