Data Science, Reinforcement Learning, Q Learning, S P 500 Index, Finance.
current_state_value[0] reward += current_state_value[1] - past_state_value[1] total_reward = total_reward + float(reward) policy.loc[current_state_value, action] = policy.loc[current_state_value, action] + \ update_policy(reward, current_state_value, action) rewards.append(total_reward)