Questions tagged [self-play]

For questions about the technique of "self play" in reinforcement learning in relation to combinatorial games and games in general.

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AlphaZero: updating policy & choosing move

I’ve been doping some research on the principles behind AlphaZero. Especially this ‘cheat sheet’(1) and this implementation(2) (in Connect4) were very useful. Yet, I still have two important ...
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27 views

How to deal with nonstationary rewards in asymmetric self-play reinforcement learning?

Suppose we're training two agents to play an asymmetric game from scratch using self play (like Zerg vs. Protoss in Starcraft). During training one of the agents can become stronger (discover a good ...
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85 views

What will Q-values look like in self-play tic-tac-toe?

This corresponds to Exercise 1.1 of RLBook, and a discussion followed from here. Considering two reward schemes- Win = +1, Draw = 0, Loss = -1 Win = +1, Draw or Loss = 0 Can we say something about ...
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72 views

How can I oppose two AI agents with keras / tensoflow?

I am trying to use tensorflow / keras to play a text based game. The game opposes two players that play by answering questions by choosing an answer among the proposed ones. Game resembles this: ...
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2answers
1k views

Why does self-playing TicTacToe not become perfect?

I trained a DQN that learns TicTacToe by playing against itself with a reward of -1/0/+1 for a loss/draw/win. Every 500 episodes I test the progress by letting it play some episodes (also 500) against ...
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2answers
224 views

How to see terminal reward in self-play reinforcement learning?

there seems to be a major difference how the terminal reward is received/handled in self-play RL vs "normal" RL which confuses me. I implemented TicTacToe the normal way, where a single agent plays ...
3
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1answer
300 views

What are good learning strategies for Deep Q-Network with opponents?

I am trying to find out what are some good learning strategies for Deep Q-Network with opponents. Let's consider the well known game Tic-Tac-Toe as an example: How should an opponent be implemented ...