cards in the playing field for a total of 28 values. Each player can choose among 200 different actions to perform in each state, each of them representing a unique combination of cards to be discarded, or a pass action.

In order for the simulated players to be able to select their actions in an intelligent manner, we needed to train artificial agents to play Chef’s Hat. For this we employ two different reinforcement learning algorithms based on Q-learning, which allow our agents to apply a temporal-difference calculation when updating the policy network and thus maximize the state transitions that will lead to the optimal reward.

The Learning Agents
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The main characteristic of the *Moody Framework* is to provide a self-assessment of the agents’ performance based on the agent’s own judgments. To better evaluate that, in our experiments, we implement three different types of agents, two learning ones based on Deep Q-Learning - DQL [@van2016deep] and on on Proximal Policy Optimization - PPO [@schulman2017proximal], and a dummy one which only select random actions. To guarantee that a taken action by one agent is valid, we use the proposed Chef’s Hat action selection greedy policy which limits each agents’ final 