### Talks to check out first:
- Introduction to Reinforcement Learning by Joelle Pineau, McGill University:
- Applications of RL.
- When to use RL?
- RL vs supervised learning
- What is MDP? Markov Decision Process
- Components of an RL agent:
- actions (Probabilistic effects)
- Reward function
- Initial state distribution
- Explanation of the Markov Property:
- Why Maximizing utility in:
- Episodic tasks
- Continuing tasks
- The discount factor, gamma γ
- What is the policy & what to do with it?
- A policy defines the action-selection strategy at every state:
- Value functions:
- The value of a policy equations are (two forms of) Bellman’s equation.
- (This is a dynamic programming algorithm).
- Iterative Policy Evaluation:
- Main idea: turn Bellman equations into update rules.
- Optimal policies and optimal value functions.
- Finding a good policy: Policy Iteration (Check the talk Below By Peter Abeel)
- Finding a good policy: Value iteration
- Asynchronous value iteration:
- Instead of updating all states on every iteration, focus on important states.
- Key challenges in RL:
- Designing the problem domain
- State representation
– Action choice
– Cost/reward signal
- Acquiring data for training
– Exploration / exploitation
– High cost actions
– Time-delayed cost/reward signal
- Function approximation
- Validation / confidence measures
- The RL lingo.
- In large state spaces: Need approximation:
- Fitted Q-iteration:
- Use supervised learning to estimate the Q-function from a batch of training data:
- Input, Output and Loss.
- i.e: The Arcade Learning Environment
Deep Q-network (DQN) and tips.
- Why Policy Optimization?
- Cross Entropy Method (CEM) / Finite Differences / Fixing Random Seed
- Likelihood Ratio (LR) Policy Gradient
- Natural Gradient / Trust Regions (-> TRPO)
- Actor-Critic (-> GAE, A3C)
- Path Derivatives (PD) (-> DPG, DDPG, SVG)
- Stochastic Computation Graphs (generalizes LR / PD)
- Guided Policy Search (GPS)
- Inverse Reinforcement Learning
- Inverse RL vs. behavioral cloning
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
- Algorithms for Reinforcement Learning.
- Reinforcement Learning and Dynamic Programming using Function Approximators.
- Reinforcement Learning by David Silver.
- Lecture 1: Introduction to Reinforcement Learning
- Lecture 2: Markov Decision Processes
- Lecture 3: Planning by Dynamic Programming
- Lecture 4: Model-Free Prediction
- Lecture 5: Model-Free Control
- Lecture 6: Value Function Approximation
- Lecture 7: Policy Gradient Methods
- Lecture 8: Integrating Learning and Planning
- Lecture 9: Exploration and Exploitation
Lecture 10: Case Study: RL in Classic Games
CS294 Deep Reinforcement Learning by John Schulman and Pieter Abbeel.
- Lecture 1: intro, derivative free optimization
- Lecture 2: score function gradient estimation and policy gradients
- Lecture 3: actor critic methods
- Lecture 4: trust region and natural gradient methods, open problems