Nintendo's Super Smash Bros. Melee fighting game can be emulated on modern hardware allowing us to inspect internal memory states, such as character positions. We created an AI that avoids being hit by training using these internal memory states and outputting controller button presses. After training on a month's worth of Melee matches, our best agent learned to avoid the toughest AI built into the game for a full minute 74.6 of the time.
DeepMind Technologies has successfully learned to play Atari 2600 games using an emulator @cite_3 . DeepMind used a convolutional neural network combined with a Q-learning variant to learn to play games using just the games' pixel output. We plan to build on this success by focusing on a relatively newer game with more complex interactions. Instead of pixels, we look at the game's internal memory, including features such as characters' positions and velocities.
@cite_3
2145339207
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.