AlphaGo & AlphaGo Zero (DL 24) | Highlights and Annotations by Gistr.

DeepMind's AlphaGo & AlphaGo Zero revolutionized Go AI. AlphaGo, using deep convolutional neural networks and reinforcement learning, beat a world champion. AlphaGo Zero improved upon this by using only self-play, surpassing its predecessor without human data. Both leveraged Monte Carlo Tree Search, but AlphaGo Zero's purely self-learned approach demonstrated the power of reinforcement learning. This segment details AlphaGo's groundbreaking use of deep learning to address the challenges of Go. It explains the roles of the value network (estimating win probability) and the policy network (assessing move quality) in guiding the search algorithm, overcoming the limitations of traditional methods. This segment contrasts the training data used for AlphaGo and AlphaGo Zero. It highlights AlphaGo's reliance on human game data versus AlphaGo Zero's use of self-play, explaining how the latter leveraged more information from self-play data for policy network training and achieved better results without human knowledge. This segment explains the Monte Carlo Tree Search (MCTS) algorithm used by both AlphaGo and AlphaGo Zero, detailing how it incorporates the policy and value networks to efficiently explore the search space. It also describes the self-play reinforcement learning process, where two MCTS agents play against each other to generate training data, iteratively improving the networks. This segment explains why Go was considered a decades-long challenge for AI, highlighting the difficulty of evaluating board positions and the vast branching factor compared to chess, making exhaustive search infeasible. The limitations of traditional AI approaches in tackling Go's complexity are clearly outlined. This segment compares the neural network architectures of AlphaGo and AlphaGo Zero. It contrasts the plain convolutional architecture of AlphaGo with the more successful residual architecture and core-head design of AlphaGo Zero, emphasizing the latter's ability to learn important board state information for both value and policy estimation.