Combining a Mixture of Experts with Transfer Learning in Complex Games
Year: 2017
Type: conference-paper
Abstract: We present a supervised approach for learning policies in a highly complex game from small amounts of human data consisting of state–action pairs. Our Neural Network architecture can adapt to the varying size of the set of legal actions, thus bypassing the need to hardcode the actions in the output layer or iterate over them. This makes the training more data efficient. We use synthetic data created via game simulations among AI agents to show that a mixture of experts, where each expert predicts actions in different portions of the game, improves performance. We then show that this approach applied to human data also improves performance: in particular, using transfer learning to enable one expert to learn from another enhances performance on those portions of the game for which there is relatively little training data compared to other portions. The domain chosen for evaluation is the board game Settlers of Catan.
Authors: Mihai Dobre, Alex Lascarides
Institution: University of Edinburgh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2788186399