Questions tagged [alphazero]

For questions related to DeepMind's AlphaZero, which is a computer program that can play Go, Chess, and Shogi. AlphaZero achieved, within 24 hours of training, a superhuman level of play in these three games by defeating world-champion programs Stockfish, Elmo, and the 3-day version of AlphaGo Zero. AlphaZero was introduced in "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" (2017) by David Silver et al.

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Where does reinforcement learning actually show up in Deepmind's game engines?

From the brief research I've done on the topic, it appears that the way Deepmind's Alphazero or Muzero makes decisions is through Monte Carlo tree searches, where in the randomized simulations allows ...
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50 views

Is Monte Carlo tree search needed in partially observable environments during gameplay?

I understand that with a fully observable environment (chess / go etc) you can run an MCTS with an optimal policy network for future planning purposes. This will allow you to pick actions for gameplay,...
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How is the rollout from the MCTS implemented in both of the AlphaGo Zero and the AlphaZero algorithms?

In a vanilla Monte Carlo tree search (MCTS) implementation, the rollout is usually implemented following a uniform random policy, that is, it takes random actions until the game is finished and only ...
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57 views

What kind of policy evaluation and policy improvement AlphaGo, AlphaGo Zero and AlphaZero are using

I'm trying to find out what kind of policy improvement and policy evaluation AlphaGo, AlphaGo Zero, and AlphaZero are using. By looking into their respective paper and SI, I can conclude that it is a ...
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51 views

Alphazero Value loss doesn't decrease

Currenly I'm trying to reimplement alphazero in pure c++ using libtorch to accomodate my project's need. But when I training my model, I found out that the value loss doesn't decrese at all after even ...
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28 views

Clarifying representation of Neural Nerwork input for Chess Alpha Zero

In the Alpha Zero paper (https://arxiv.org/pdf/1712.01815.pdf) page 13, the input for the NN is described. In the beggining of the page, the authors state that: "The input to the Neural Network ...
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35 views

What is the consensus on the “correct” temperature settings for the AlphaZero algorithm?

In the AlphaZero learning algorithm, during self-play to generate training games, the move played is chosen with probability proportional to the MCTS visits raised to the $\tau$-th power, where $\tau$ ...
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29 views

Are inputs into AlphaZero the same during the evaluate step in MCTS and during test time?

From the AlphaZero paper: The input to the neural network is an N × N × (M T + L) image stack that represents state using a concatenation of T sets of M planes of size N × N . Each set of planes ...
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47 views

Stack of Planes as the Action Space Representation for AlphaZero (Chess)

I have a question regarding the action space of the policy network used in AlphaZero. From the paper: We represent the policy π(a|s) by a 8 × 8 × 73 stack of planes encoding a probability ...
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33 views

How to encode board before input into the neural net?

Currently I'm working on an educational project (implementation of AlphaZero approach to different types of board games). My biggest concern at the moment is how to encode board before input into the ...
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26 views

AlphaZero value at root node not being affected by training

I have written my own AlphaZero implementation and started training it recently. Problem is, I am 99% sure there is a mistake and I do not know how to tackle this, since I cannot explain it. I am new ...
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40 views

Alpha Zero does not converge for Connect 6, a game with huge branching factor - why?

I have a problem with applying alpha zero self-play to a game (Connect 6) with a huge branching factor (30,000 on average). I have implemented the MCTS as described but I found that during the MCTS ...
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39 views

In AlphaZero, which features are one-hot encoded and which are single real-valued?

From the AlphaZero paper, the caption of Table S1 (p. 13) Table S1: Input features used by AlphaZero in Go, Chess and Shogi respectively. The first set of features are repeated for each position in a ...
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75 views

What would be the AlphaGo's performance in continuous action space?

During my research for Google DeepMind's Go-playing program Alpha Go and its successor Alpha Go Zero, I discovered that the system uses a clever pipeline and an interplay of blocks of both policy and ...
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56 views

Total loss increasing, but loss components are decreasing?

I am writing a AlphaGo Zero clone, and sometimes in the training the policy head loss and value head loss would both be decreasing, but the total loss is increasing? How is this possible? I am using ...
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77 views

What's missing from Alpha Zero to make it generally intelligent?

Alpha Zero is the game playing AI. One might try to use the algorithm in a robot which takes input from it's environment and thinks about taking the best course of action. So I was thinking about ...