Reinforcement Learning, Artificial Intelligence, Machine Learning, Data Science, Statistics.
next_state = env.initial_step(curr_state, curr_action) #returned state_tue add_to_dict(next_state,valid_actions(next_state)) curr_state = next_state #state_tue became state(t-1) #note that curr_state is always previous day evening's stock total_reward = 0 while time_step < STEPS: #the episode will