Reinforcement Learning, Deep Learning, Artificial Intelligence, Data Science.
γ=0.99, it will ignore rewards that are 100 timesteps away. But if the rewards are given more frequently, γ can be lower. If TD(λ) is used in calculating rewards, we can use a higher value for γ. The λ value blends TD with Monte Carlo to reduce variance. Monte Carlo has no bias but has high