Adaptive AI Engine for RTS Games

Discussing the theory and practice

Paper Analysis : Adaptive reinforcement learning agents in RTS games 2008 – 24 June 2009

Posted by merothehero on June 25, 2009

Adaptive reinforcement learning agents in RTS games

-paper url: http://mzrts.files.wordpress.com/2009/06/adaptive-reinforcement-learning-agents-in-rts-games-2008.pdf

-game implemented using:  2apl – agent programming language

-Uses Dynamic Scripting instead of static scripting which is used in most commercial games.

-uses BDI Agents: Beliefs, Desires and Intentions.

-Uses Monte-Carlo Learning Process.

-Implicit adaptation: adaptation from the beginning of the game by including opponent’s data

-Explicit adaptation: adaptation as a result of opponent’s actions.

-Integrates agent technology and reinforcement learning-

Future Research in Reinforcement Learning:

1-Faster Learning algorithms are wanted, in order to make agents learning from the entire environment (not only from a small portion of the environment as implanted).

2-Learning shall cover also low-level actions not only high-level strategy actions.

3-multi-task learning agents aren’t available

4-a strategy visualization tool would be added to our goals.

5-Explicit Player modeling (WHAT WE THOUGHT ABT)

6-The Framework can be a base for research in the following :

  • High-performance reinforcement learning and planning
  • multi-agent communication and negotiation
  • agent societies
  • Data mining and human player modeling.
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One Response to “Paper Analysis : Adaptive reinforcement learning agents in RTS games 2008 – 24 June 2009”

  1. abdelrahmanogail said

    Thanks man. el7 I’ve understand the big picture of it. I’ve only one questions what is “agent societies”?

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