View source for 20240017175. Adversarial Reinforcement Learning for Procedural Content Generation and Improved Generalization simplified abstract (Electronic Arts Inc.)

=Adversarial Reinforcement Learning for Procedural Content Generation and Improved Generalization=


==Organization Name==

[[:Category:Electronic Arts Inc.|Electronic Arts Inc.]]


[[Category:Electronic Arts Inc.]]

==Inventor(s)==

[[:Category:Linus Mathias Gissl�n of Enskede gard (SE)|Linus Mathias Gissl�n of Enskede gard (SE)]][[Category:Linus Mathias Gissl�n of Enskede gard (SE)]]

[[:Category:Andrew John Eakins of Guildford (GB)|Andrew John Eakins of Guildford (GB)]][[Category:Andrew John Eakins of Guildford (GB)]]

==Adversarial Reinforcement Learning for Procedural Content Generation and Improved Generalization - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240017175 titled 'Adversarial Reinforcement Learning for Procedural Content Generation and Improved Generalization

==Simplified Explanation==

Methods, apparatus, and systems are provided for training two reinforcement-learning (RL) agents in a computer game environment using RL techniques. The first RL agent generates a sub-goal sequence in relation to an overall goal within the game environment. After the second RL agent successfully achieves a current sub-goal in the sequence, the first RL agent generates a new sub-goal. The second RL agent iteratively interacts with the game environment to achieve the current sub-goal. The first RL agent is updated with a reward when the second RL agent successfully achieves the current sub-goal, and the second RL agent is updated based on a reward issued by the game environment. Once trained, the first RL agent can automatically generate procedural content in the game environment, and the second RL agent can automatically interact with a procedural content generation (PCG) game environment.

* The patent describes a method for training two RL agents in a computer game environment.
* The first RL agent generates a sub-goal sequence in relation to an overall goal in the game environment.
* The second RL agent interacts with the game environment to achieve the current sub-goal in the sequence.
* The first RL agent is updated with a reward when the second RL agent successfully achieves the current sub-goal.
* The second RL agent is updated based on a reward issued by the game environment.
* Once trained, the first RL agent can automatically generate procedural content in the game environment.
* The second RL agent can automatically interact with a PCG game environment.

== Potential Applications ==
* Automatic procedural content generation in computer games.
* Training RL agents to interact with game environments.

== Problems Solved ==
* Manual generation of procedural content in computer games.
* Manual interaction with game environments by RL agents.

== Benefits ==
* Improved efficiency and speed in generating procedural content.
* Enhanced gameplay experience through automated RL agent interaction.


==Original Abstract Submitted==

methods, apparatus and systems are provided for training a first reinforcement-learning (rl) agent and a second rl agent coupled to a computer game environment using rl techniques. the first rl agent iteratively generates a sub-goal sequence in relation to an overall goal within the computer game environment, where the first rl agent generates a new sub-goal for the sub-goal sequence after a second rl agent, interacting with the computer game environment, successfully achieves a current sub-goal in the sub-goal sequence. the second rl agent iteratively interacts with the computer game environment to achieve the current sub-goal in which each iterative interaction includes an attempt by the second rl agent for interacting with the computer game environment to achieve the current sub-goal. the first rl agent is updated using a first reward issued when the second rl agent successfully achieves the current sub-goal. the second rl agent is updated when a second reward is issued by the computer game environment based on the performance of the second rl agent attempting to achieve said current sub-goal. once validly trained, the first rl agent forms a final first rl agent for automatic procedural content generation (pcg) in the computer game environment and the second rl agent forms a final second rl agent for automatically interacting with a pcg computer game environment.



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