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Preparing the books to read every day is enjoyable for many people. However, there are still many people who also don't like reading. This is a problem. But, when you can support others to start reading, it will be better. One of the books that can be recommended for new readers is genetic programming iii darwinian invention and problem solving. This book(More)
Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefitof exploration can be estimated using the classical notion of Value of Information — the expected improvement in future decision quality arising from the information acquired by exploration.(More)
The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the(More)
Safe state abstraction in reinforcement learning allows an agent to ignore aspects of its current state that are irrelevant to its current decision, and therefore speeds up dynamic programming and learning. This paper explores safe state abstraction in hierarchical reinforcement learning, where learned behaviors must conform to a given partial, hierarchical(More)
We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes standard features such as parameterized subroutines, temporary interrupts, aborts, and memory variables, but also allows for unspecified choices in the agent program. For learning(More)
It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various human-written algorithms have appeared in the past two decades for the vexatious majority classification task for one-dimensional(More)
This paper describes an automated process for designing electrical circuits in which "What You Want Is What You Get" ("WYWIWYG" – pronounced "wow-eee-wig"). The design process uses genetic programming to produce both the topology of the desired circuit and the sizing (numerical values) for all the components of a circuit. Genetic programming successfully(More)
Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent’s limited computational resources to achieve a good estimate of the value of environment states. To choose effectively where to spend a costly planning step, classic prioritized sweeping uses a simple heuristic to focus computation on the states that are(More)
This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers(More)