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AI planning in the imagination: High-level planning on learned abstract search spaces
However, in real-world environments, the model with respect to which the agent plans has been constrained to be grounded in the real environment itself, as opposed to a more abstract model which allows for planning over compound actions and behaviors.
Finding Nash equilibria by minimizing approximate exploitability with learned best responses
Most of that work has focused on games with finite, discrete action spaces.
Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks
Being able to model such mixed strategies is crucial for tackling continuous-action games that lack pure-strategy equilibria.
Efficient exploration of zero-sum stochastic games
We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay, such as in financial or military simulations and computer games.
