Reinforcement Learning, Artificial Intelligence, Machine Learning, Data Science, Statistics.
run only for a few steps and not infinitely #curr_action is a_tue and is a(t-1) curr_action = epsilon_greedy(curr_state, episode) next_state, reward = env.step(curr_state, curr_action) #next_state is state_wed add_to_dict(next_state, valid_actions(next_state)) # UPDATE RULE max_next =