Based of reinforcement learning theory, introduce an agent that perform an action only with the current state of the game.
For that, it should be trained before to play, using the following knowledged:
The goal will be to find an RL-based agent that competes against greedy algorithm, and that reach to win more than 80% of the time. Different learning functions (Q-learning, policy, mix, others) can be tried.
Note: a brief summary could be regard on: https://arxiv.org/pdf/1708.05866.pdf
Based of reinforcement learning theory, introduce an agent that perform an action only with the current state of the game.
For that, it should be trained before to play, using the following knowledged:
The goal will be to find an RL-based agent that competes against greedy algorithm, and that reach to win more than 80% of the time. Different learning functions (Q-learning, policy, mix, others) can be tried.
Note: a brief summary could be regard on: https://arxiv.org/pdf/1708.05866.pdf