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MIND T3_player agent: A Self-Taught Agent in a Tic-Tac-Toe environment

The T3-Player is a self-taught agent who inhabits a dynamic tic tac toe environment where a differential-difference (time-lag) process has to be solved. Each process is an episode (match) during which two agents O an X randomly play against each other by alternating turn. There exist auxiliary, background routines belonging to the environment that checks for winning positions and return reward information to the environment. 

T3-Player is an agent from the root and satisfies the conditions:

  • Perceives its environments through sensors (board state neurons).
  • Acts upon that environment through effectors (token insertion neurons).
  • Act autonomously (i-agent neurons).
  • Realize a set of goals or tasks to learn to play top-level games. (Gradient Descend and Reinforcement Learning).

 

The current T3-Player model is constructed and delivery in windows Borland C-C++, this is an easy to install, friendly ambient that produces stable, stand-alone .exe code. Linux C version can be directly constructed using our C code and converting the graphic routines from Borland C to Linux C (contributions are welcome).

As a noticeable advantage, our C code runs almost directly in ARDUINO boards. (running code coming soon).

The overall T3-Player agent system for Tic-Tac-Toe is delivered in three modules. You can download each module in the following sections:

MODULE 1

The virtual Tic-Tac-Toe board is brought to life and two random agents play against each other.

MODULE 2

The neural networks and gradient descend come into action.

MODULE 3

Bellman equation and the self-taught agents appears, creating human level artificial players.

Challenge

Using module 1 the entire agent can be assembled. Try it!

If you succeed, send us an email to contact.us@mind-researchgroup.com
and you will receive a prize.

Multi joint robot driven by a self-taught agent

In previous research we proposed and develop computer models written in C++, based on Artificial Intelligence, for a self-motivated, multi-joint virtual robot driven by an agent that learns by itself to control the robot’s body and execute complex folding motions that produce intelligent mass displacements in multifactorial environments [3][4].

The robot’s mechanical joints, muscles and sensors are controlled by trainable artificial neurons and its abilities to execute challenging mechanical work are acquired by a self-taught agent, a computer program capable of exploring and learn new solutions by itself through a combination of:

  • Artificial Neural Net.
  • Gradient Descent.
  • Reinforcement Learning. Algorithms.

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