Keras Reinforcement Learning Projects

Monte Carlo methods

The Monte Carlo (MC) methods for estimating the value function and discovering excellent policies do not require the presence of a model of the environment. They are able to learn through the use of the agent's experience alone or from samples of state sequences, actions, and rewards obtained from the interactions between agent and environment. The experience can be acquired by the agent in line with the learning process or emulated by a previously populated dataset. The possibility of gaining experience during learning (online learning) is interesting because it allows obtaining excellent behavior even in the absence of a priori knowledge of the dynamics of the environment. Even learning through an already populated experience ...