---
title: "General Game Learning Using Knowledge Transfer"
authors:
  - "Bikramjit Banerjee"
  - "Peter Stone"
doi: ""
url: "https://ijcai.org/proceedings/2007/107"
pdf: "https://ijcai.org/proceedings/2007/0107.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "672-676"
track: ""
keywords:
---

# General Game Learning Using Knowledge Transfer

Bikramjit Banerjee, Peter Stone

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 672-676.

## Abstract

We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other games. We use the technique of value-function transfer where general features are extracted from the state space of a previous game and matched with the completely different state space of a new game. To capture the underlying similarity of vastly disparate state spaces arising from different games, we use a game-tree lookahead structure for features. We show that such feature-based value function transfer learns superior policies faster than a reinforcement learning agent that does not use knowledge transfer. Furthermore, knowledge transfer using lookahead features can capture opponent-specific value-functions, i.e. can exploit an opponent's weaknesses to learn faster than a reinforcement learner that uses lookahead with minimax (pessimistic) search against the same opponent.