Senior / Mid Game Designer in Staffbit

Creation of a system that allows for the simulation of gameplay in many game economy configurations, where a simulated player - an agent - with autonomous decision-making skills interacts with the environment.

Using reinforcement learning artificial intelligence algorithms, in particular PPO and IMPALA. The trained AI agent will be a neural network that selects an action from a finite set that maximizes the expected reward. The agent will have limitations identical to the physical limitations of a real player, such as limited visible information and limitations on performing actions. The reward will be calculated based on your game score. The trained agent will allow you to evaluate the given configuration of the game economy in which it was trained.

An optimal configuration will be selected in terms of the rules of good game design, which will be defined in consultation with game design experts, so that the player reaches the expected level in the game after a certain time or a certain number of interactions.

The goal of the next phase is to obtain a model that will improve monetization by at least 10%. The end result of the project will be the implementation of innovative technology developed in the course of R&D work in the form of a set of tools for simulating the game economy without the need to use historical player data, to support the game designer in selecting optimal game parameters and to generate personalized offers. The developed technology will combine techniques for storing and analyzing large data sets and machine learning.

• Integration of currencies with other elements of the game economy, such as rewards, events, and items, to ensure system consistency.
• An analysis of the current in-game currency systems and how various items are currently valued.
• Defining the main goals of standardization, e.g. simplifying the game economy, making it easier for players to understand the value of items, better balancing the game economy.
• Development of generic game mechanics
• Analysis and work on improving player conversion
• Creation and testing of economic simulation scenarios
• Work related to flow mapping the so-called hard and soft currency that will be common to the mobile gaming economy
• Analysis of gameplay in various free 2 play games in order to create a generic core loop model
• Work related to generic pvp gameplay systems between players - Categorization of game elements depending on their role in the game

Creation of a system enabling simulation of gameplay in many game economy configurations, where a simulated player - agent - with autonomous decision-making skills interacts with the environment .

Using reinforcement learning artificial intelligence algorithms, in particular PPO and IMPALA. The trained AI agent will be a neural network that selects an action from a finite set that maximizes the expected reward. The agent will have limitations identical to the physical limitations of a real player, such as limited visible information and limitations on performing actionstion. The reward will be calculated based on your game score. The trained agent will allow you to evaluate the given configuration of the game economy in which it was trained.

An optimal configuration will be selected in terms of the rules of good game design, which will be defined in consultation with game design experts, so that the player reaches the expected level in the game after a certain time or a certain number of interactions.

The goal of the next phase is to obtain a model that will improve monetization by at least 10%. The end result of the project will be the implementation of innovative technology developed in the course of R&D work in the form of a set of tools for simulating the game economy without the need to use historical player data, to support the game designer in selecting optimal game parameters and to generate personalized offers. The developed technology will combine techniques for storing and analyzing large data sets and machine learning.