Project Paidia is a research project in close collaboration between Microsoft Research Cambridge and Ninja Theory. Its focus is to drive state of the art research in reinforcement learning to enable novel applications in modern video games, in particular: agents that learn to collaborate with human players.
In contrast to traditional approaches to crafting the behavior of bots, non-player characters, or other in-game characters, reinforcement learning does not require a game developer to anticipate a wide range of possible game situations and map out and code all required behaviors. Instead, with reinforcement learning, game developers control a reward signal which the game character then learns to optimize while responding fluidly to all aspects of a game’s dynamics. The result is nuanced situation and player-aware emergent behavior that would be challenging or prohibitive to achieve using traditional Game AI.
Project Paidia demo – Learning to collaborate in Bleeding Edge, footage of trained Project Paidia agents. Not representative of final game gameplay or visuals.
Project Paidia focuses on learning a particularly challenging type of behavior: collaboration with human players. Because human players are notoriously creative and hard to predict, creating the experience of genuine collaboration towards shared goals has long been elusive. Together with colleagues at Ninja Theory, the MSR team identified a perfect test bed for driving this research, Ninja Theory’s latest game Bleeding Edge. Bleeding Edge is a team-based game, and includes a range of characters that have to work together to score points and defeat their opponents. In their latest demo, the team showcases how reinforcement learning enables agents to learn to coordinate their actions.
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