- Understand TD(0) for prediction
- Understand SARSA for on-policy control
- Understand Q-Learning for off-policy control
- Understand the benefits of TD algorithms over MC and DP approaches
- Understand how n-step methods unify MC and TD approaches
- Understand the backward and forward view of TD-Lambda
- TD-Learning is a combination of Monte Carlo and Dynamic Programming ideas. Like Monte Carlo, TD works based on samples and doesn't require a model of the environment. Like Dynamic Programming, TD uses bootstrapping to make updates.
- Whether MC or TD is better depends on the problem and there are no theoretical results that prove a clear winner.
- General Update Rule:
Q[s,a] += learning_rate * (td_target - Q[s,a]).td_target - Q[s,a]is also called the TD Error. - SARSA: On-Policy TD Control
- TD Target for SARSA:
R[t+1] + discount_factor * Q[next_state][next_action] - Q-Learning: Off-policy TD Control
- TD Target for Q-Learning:
R[t+1] + discount_factor * max(Q[next_state]) - Q-Learning has a positive bias because it uses the maximum of estimated Q values to estimate the maximum action value, all from the same experience. Double Q-Learning gets around this by splitting the experience and using different Q functions for maximization and estimation.
- N-Step methods unify MC and TD approaches. They making updates based on n-steps instead of a single step (TD-0) or a full episode (MC).
Required:
- Reinforcement Learning: An Introduction - Chapter 6: Temporal-Difference Learning
- David Silver's RL Course Lecture 4 - Model-Free Prediction (video, slides)
- David Silver's RL Course Lecture 5 - Model-Free Control (video, slides)
Optional:
- Reinforcement Learning: An Introduction - Chapter 7: Multi-Step Bootstrapping
- Reinforcement Learning: An Introduction - Chapter 12: Eligibility Traces
- Get familiar with the Windy Gridworld Playground
- Implement SARSA
- Get familiar with the Cliff Environment Playground
- Implement Q-Learning in Python