IA : Deep Reinforcement learning. A mimicry of Human evolution?

DRL is an AI technique that aims to take appropriate actions to maximise reward in a certain situation (game/simulation/reality). Before further explaining, it is necessary to give some definitions: - Agent: It is the "player" of the game, the entity who's taking actions, he follows a strategy (called policy) to evolve in the environment. His ultimate goal is to maximize his reward. The environment is said to be in a state s at a given time - Policy: It is the strategy which drives the Agent actions, it is designed by a NN. The policy can change as the Agent learns from his experiences - Reward: A metric aiming to determine the performance of the Agent's actions within the environment Now let's take an example to illustrate the mecanisms of DRL: The famous card game of Poker Texas Hold'em (PTH). In PTH, the agents are the players and the environment is the set of rules of PTH (blinds, number of cards, minimum bet, playing order…).