Research and Implementation of Intelligent Decision Based on a Priori Knowledge and DQN Algorithms in Wargame Environment

The reinforcement learning problem of complex action control in a multi-player wargame has been a hot research topic in recent years. In this paper, a game system based on turn-based confrontation is designed and implemented with state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based on the DQN (Deep Q Network) to model complex game behaviors. Then, an a priori knowledge-based algorithm PK-DQN (Prior Knowledge-Deep Q Network) is introduced to improve the DQN algorithm, which accelerates the convergence speed and stability of the algorithm. The experiments demonstrate the correctness of the PK-DQN algorithm, it is validated, and its performance surpasses the conventional DQN algorithm. Furthermore, the PK-DQN algorithm shows effectiveness in defeating the high level of rule-based opponents, which provides promising results for the exploration of the field of smart chess and intelligent game deduction.

Download Full-text

A DIGITAL PERCEPTRON LEARNING IMPLEMENTATION WITH LOOK-UP TABLE FEEDBACK LAYER

Journal of Circuits System and Computers ◽

10.1142/s021812669600008x ◽

1996 ◽

Vol 06 (01) ◽

pp. 79-84

Author(s):

D. ZHANG ◽

M.I. ELMASRY

Keyword(s):

Learning Algorithm ◽

A Priori ◽

A Priori Knowledge ◽

Look Up Table ◽

Layered Networks ◽

Perceptron Learning ◽

Priori Knowledge

In this paper, a digital perceptron learning structure using a priori knowledge inherent in layered networks is presented. Its implementation mechanism is accompanied by the corresponding look-up table technology in VLSI medium. Compared with the conventional learning algorithm, it is shown that this structure can reduce the complexity of implementation in VLSI while maintaining the same performance.

Download Full-text

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2015.08.011 ◽

2015 ◽

Vol 46 ◽

pp. 82-92 ◽

Cited By ~ 14

Author(s):

Emma M. Sánchez ◽

Julio B. Clempner ◽

Alexander S. Poznyak

Keyword(s):

Reinforcement Learning ◽

A Priori ◽

A Priori Knowledge ◽

The Mean ◽

Bank Marketing ◽

Mean Variance ◽

Priori Knowledge

Download Full-text

A formal methods approach to interpretable reinforcement learning for robotic planning

Science Robotics ◽

10.1126/scirobotics.aay6276 ◽

2019 ◽

Vol 4 (37) ◽

pp. eaay6276 ◽

Cited By ~ 6

Author(s):

Xiao Li ◽

Zachary Serlin ◽

Guang Yang ◽

Calin Belta

Keyword(s):

Reinforcement Learning ◽

Formal Methods ◽

Learning Algorithm ◽

A Priori ◽

Generation Process ◽

Learning Approaches ◽

Learning Agent ◽

Domain Specific Knowledge ◽

Robotic Planning ◽

High Level

Growing interest in reinforcement learning approaches to robotic planning and control raises concerns of predictability and safety of robot behaviors realized solely through learned control policies. In addition, formally defining reward functions for complex tasks is challenging, and faulty rewards are prone to exploitation by the learning agent. Here, we propose a formal methods approach to reinforcement learning that (i) provides a formal specification language that integrates high-level, rich, task specifications with a priori, domain-specific knowledge; (ii) makes the reward generation process easily interpretable; (iii) guides the policy generation process according to the specification; and (iv) guarantees the satisfaction of the (critical) safety component of the specification. The main ingredients of our computational framework are a predicate temporal logic specifically tailored for robotic tasks and an automaton-guided, safe reinforcement learning algorithm based on control barrier functions. Although the proposed framework is quite general, we motivate it and illustrate it experimentally for a robotic cooking task, in which two manipulators worked together to make hot dogs.

Download Full-text

Seeing, Knowing, and Doing

10.1093/oso/9780197503508.001.0001 ◽

2020 ◽

Author(s):

Robert Audi

Keyword(s):

Practical Reasoning ◽

Practical Knowledge ◽

A Priori ◽

Human Action ◽

A Priori Knowledge ◽

Rational Action ◽

Reasons For Action ◽

Discriminative Response ◽

Rich Information ◽

Priori Knowledge

This book provides an overall theory of perception and an account of knowledge and justification concerning the physical, the abstract, and the normative. It has the rigor appropriate for professionals but explains its main points using concrete examples. It accounts for two important aspects of perception on which philosophers have said too little: its relevance to a priori knowledge—traditionally conceived as independent of perception—and its role in human action. Overall, the book provides a full-scale account of perception, presents a theory of the a priori, and explains how perception guides action. It also clarifies the relation between action and practical reasoning; the notion of rational action; and the relation between propositional and practical knowledge. Part One develops a theory of perception as experiential, representational, and causally connected with its objects: as a discriminative response to those objects, embodying phenomenally distinctive elements; and as yielding rich information that underlies human knowledge. Part Two presents a theory of self-evidence and the a priori. The theory is perceptualist in explicating the apprehension of a priori truths by articulating its parallels to perception. The theory unifies empirical and a priori knowledge by clarifying their reliable connections with their objects—connections many have thought impossible for a priori knowledge as about the abstract. Part Three explores how perception guides action; the relation between knowing how and knowing that; the nature of reasons for action; the role of inference in determining action; and the overall conditions for rational action.

Download Full-text

How Reality is Reasonable

10.1093/oso/9780198810384.003.0006 ◽

2018 ◽

Author(s):

Donald C. Williams

Keyword(s):

A Priori ◽

A Priori Knowledge ◽

Dimensional Manifold ◽

The World ◽

Being There ◽

Ways Of Being ◽

Fundamental Entity ◽

Spatiotemporal Relations ◽

Priori Knowledge

This chapter begins with a systematic presentation of the doctrine of actualism. According to actualism, all that exists is actual, determinate, and of one way of being. There are no possible objects, nor is there any indeterminacy in the world. In addition, there are no ways of being. It is proposed that actual entities stand in three fundamental relations: mereological, spatiotemporal, and resemblance relations. These relations govern the fundamental entities. Each fundamental entity stands in parthood relations, spatiotemporal relations, and resemblance relations to other entities. The resulting picture is one that represents the world as a four-dimensional manifold of actual ‘qualitied contents’—upon which all else supervenes. It is then explained how actualism accounts for classes, quantity, number, causation, laws, a priori knowledge, necessity, and induction.

Download Full-text

How Do We Know that We’re Not Brains in Vats?

10.1093/oso/9780199564477.003.0007 ◽

2018 ◽

Author(s):

Keith DeRose

Keyword(s):

Epistemic Justification ◽

A Priori ◽

A Priori Knowledge ◽

Conservative Approach ◽

Contingent Fact ◽

Brains In Vats ◽

Priori Knowledge

In this chapter the contextualist Moorean account of how we know by ordinary standards that we are not brains in vats (BIVs) utilized in Chapter 1 is developed and defended, and the picture of knowledge and justification that emerges is explained. The account (a) is based on a double-safety picture of knowledge; (b) has it that our knowledge that we’re not BIVs is in an important way a priori; and (c) is knowledge that is easily obtained, without any need for fancy philosophical arguments to the effect that we’re not BIVs; and the account is one that (d) utilizes a conservative approach to epistemic justification. Special attention is devoted to defending the claim that we have a priori knowledge of the deeply contingent fact that we’re not BIVs, and to distinguishing this a prioritist account of this knowledge from the kind of “dogmatist” account prominently championed by James Pryor.

Download Full-text