Browsing by Author "Kopio, Ville"

Coupled empowerment maximization (CEM) is an action selection policy for artificial agents. It utilizes the empowerment values of different agents in order to determine the best possible action to take. Empowerment quantifies the potential an agent has to impact the world around it. For example, an agent in an open field has a higher empowerment compared to an agent that is locked in a cage as in an open field the agent has a higher freedom of movement. This kind of action selection policy does not rely on the agent behavior to be explicitly programmed which makes it particularly promising as a non-player character policy for procedurally generated video games. To research the feasibility of CEM agents in practice, they should be studied in a large variety of different situations and games. Some studies have already been performed with a CEM agent that is implemented in the Python programming language. The agent ran in small game environments built on top of the Griddly game engine, but the computational performance of the agent has not been the focus. Scaling the experiments to larger environments using the old implementation is not feasible as conducting the experiments would take an enormous amount of time. Thus, the focus in this thesis is on lowering the time complexity of the agent so that there are more avenues for further research. This is reached with a new CEM agent implementation that (1) has a more modular architecture making future changes easier, (2) simulates future game states with an improved forward model which keeps track of already visited game states, and (3) uses an optimized Griddly version which has improved environment cloning performance. Our approach is around 200 times faster compared to the old implementation using environments and parametrization that potentially are used in future quantitative and qualitative experiments. The old implementation also has some bugs that are now resolved in the new implementation.