Each notebook contains the content and code-along of each session. We recommend that you run the notebooks from Google Colaboratory for minimal setup requirements. Edit the Fill in The Code section for coding assigments and check with our way of solving them in solutions.
Markov Decision Processes / Discrete States and Actions
- What is Reinforcement Learning: Pavlov's kitties
- How Useful is Reinforcement Learning: games, robotics, ads biddings, stock trading, etc.
- Why is Reinforcement Learning Different: level of workflow automation in classes of machine learning algorithm
- Use cases for reinforcement learning
- Reinforcement Learning Framework and Markov Decision Processes
- GridWorld example to explain:
- Problems: Markov decision processes, states, actions, and rewards
- Solutions: policies, state values, (state-)action values, discount factor, optimality equations
- Words of Caution: a few reasons Deep Reinforcement Learning Doesn't Work Yet
- Challenges:
- Read up on Bellman's equations and find out where they hid in our workshop today.
- What are you ideas about how we can find the policy policy?
- Play around with Gridworld. Tweak these variables and see what happens to state and action values:
- Expand the grid and/or add some more traps
- Wind probability
- Move rewards
- Discount factor
- Epsilon and how to decay it (or not)
Session 2 Win Big at Monte Carlo - Sponsored by Humanize, the company that helps your business grow with AI
Discrete States and Actions
- Blackjack-v0 environment, human play and computer play
- Optimal Strategy for Blackjack
- What is Monte Carlo Method
- Monte Carlo Prediction
- Monte Carlo Control: All-visit, First-visit, and GLIE
- Challanges:
- What are some other ways of solving reinforcement learning problems? How are they better or worse than Monte Carlo methods e.g. performance, data requirements, etc.?
- Solve at least one of the following OpenAI gym environments with discrete states and actions:
- FrozenLake-v0
- Taxi-v2
- Blackjack-v0
- Any other environments with discrete states and actions at OpenAI Gym
- Check
session2b.ipynbif you are interested in using Monte Carlo method to solve Grid World. This will give you more insight into difference between all-visit and first-visit Monte Carlo.
Session 3 GET a Taxi with Temporal Difference Learning - Sponsored by GET, the new ride-hailing service in Thailand
Discrete States and Actions
- Taxi-v2 environment
- Comparison between Monte Carlo and TD
- SARSA
- Q-learning
- Expected SARSA
- Handling Continuous States
- Challenges: Solve an environment with continuous states using discretization
- Acrobat-v1
- MountainCar-v0
- CartPole-v0
- LunarLander-v2
- Points to consider:
- What are other ways of handling continuous states? (See tile coding)
- What are the state space, action space, and rewards of the environment?
- What algorithms did you use to solve the environment and why?
- How many episodes did you solve it in? Can you improve the performance? (Tweaking discount factor, learning rate, Monte Carlo vs TD)
Optional
- Building Blocks
Familiarize ourselves with basic building blocks of a neural network in PyTorch such as tensors and layers
- Your First Neural Network
Build your first neural network with the main components of architecture, loss and optimizer
- Spiral Example
Use your first neural network in a task challenging for linear models to understand why we even need deep learning
Continuous States and Discrete Actions
- Replacing Q dictionaries with neural networks
- LunarLander-v2 environment
- Vanilla Policy Gradient aka Monte Carlo Policy Gradient aka REINFORCE aka Stochastic Policy Gradient
- Train Your Own Vanilla Policy Gradient Agent:
- Hyperparameter tuning
- Reward engineering
- Inside Policy Gradient Agent:
- Policy network
- Returns function
- Trajectories
- Gradient ascent
- Bonus: How to Derive Gradients of Policy Network
- Challenges:
- Finetune the model and try to beat OpenAI Leaderboard at 658 episodes. Pay attention on how you can improve on vanilla policy gradients such as reward shaping.
- See if you can solve
LunarLanderContinuous-v2with continuous actions using more sophisticated policy gradient methods such as TRPO and PPO.
Continuous States and Continuous Actions
- PPO
- Parallel environments
- Normalized rewards and actions
- Future rewards
- GAE rewards
- Clipped surrogate function
- Challenges: Implement PPO to solve
LunarLanderContinuous-v2and compare it to your last project
Session 5 Deep Deep Q-learning to Drive MountainCar - Sponsored by GET, the new ride-hailing service in Thailand
Continuous States and Discrete Actions
- MountainCar-v0 environment
- Deep Q-learning (DQN)
- Train Your Own DQN Agent:
- Hyperparameter tuning
- Reward engineering
- Inside DQN Agent:
- Replay Memory
- Q Networks
- Agent action selection
- Agent update: DQN and DDQN
- Challenges:
- Finetune the model and try to beat OpenAI Leaderboard at 341 episodes. Use what you learn from this session such as creative reward engineering and other hyperparameter tunings.
- Try to figure out how you can solve
MountainCarContinuous-v0. It is almost exactly the same asMountainCar-v0but with continuous action space of size 1. See NAF Q-learning and DDPG papers for some hints. - Read up on Rainbow and how to push DQN to its limits.
Continuous States and Discrete Actions
- Rainbow
- Vanilla DQN (experience replay + target network)
- Double DQN
- Prioritized experience replay
- Dueling networks
- Multi-step learning
- Distributional RL
- Noisy networks
- Challenges: Implement Rainbow to solve
MountainCarContinuous-v0and compare it to your last project
Continuous States and Continuous Actions
-
Policy-based vs Value-based Methods
-
Pendulum-v0 environment
-
Deep Deterministic Policy Gradient (DDPG)
-
Train Your Own DDPG Agent:
- Hyperparameter tuning
- Reward engineering
-
How DDPG Agent Learns
- Critic update
- Actor update
-
Challenges:
-
Try to beat OpenAI leaderboard 100-episode average of -123.11 ± 6.86 for
Pendulum-v0 -
Implement DDPG to solve MountainCarContinuous-v0
-
What are other methods that can handle continuous action space except for DDPG? Look up Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC).
Continuous States and Continuous Actions
- A2C / A3C
- PPO
- SAC
- Explore vs exploit: epsilon greedy, ucb, thompson sampling
- Reward function setting
- Monte Carlo Tree Search
- Hackathon nights to play Blackjack, Poker, Pommerman, boardgames and self-driving cars
- Sutton and Barto
- Stanford CS234
- Udacity RL Nanodegree
- David Silver Lectures
- UC Berley Lectures
- Siraj's Move 37
- Denny Britz Repo
- Intro to RL in Trading
- Spinning Up - an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL)
- OpenAI Gym - a toolkit for developing and comparing reinforcement learning algorithms
- Unity ML-Agent Toolkit - an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents
- Holodeck - a high-fidelity simulator for reinforcement learning built on top of Unreal Engine 4
- AirSim - a simulator for drones, cars and more, built on Unreal Engine
- Carla - an open-source simulator for autonomous driving research
- Pommerman - a clone of Bomberman built for AI research
- MetaCar - a reinforcement learning environment for self-driving cars in the browser
- Boardgame.io - a boardgame environment
- Unity ML-Agent Toolkit - an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents
- SLM Labs - a modular deep reinforcement learning framework in PyTorch
- Dopamine - a research framework for fast prototyping of reinforcement learning algorithms
- TRF - a library built on top of TensorFlow that exposes several useful building blocks for implementing Reinforcement Learning agent
- Horizon - an open source end-to-end platform for applied reinforcement learning (RL) developed and used at Facebook.