Hands-On Reinforcement Learning for Games by Micheal Lanham -- Read -- Imperial Library of Trantor

Refreshing the environment Partially observable Markov decision process Constructing DQN The replay buffer The DQN class Calculating loss and training

Exercising DQN

Revisiting the LunarLander and beyond

Exercises Summary

Going Deeper with DDQN

Understanding visual state

Encoding visual state

Introducing CNNs Working with a DQN on Atari

Adding CNN layers

Introducing DDQN

Double DQN or the fixed Q targets Dueling DQN or the real DDQN

Extending replay with prioritized experience replay Exercises Summary

Policy Gradient Methods

Understanding policy gradient methods

Policy gradient ascent

Introducing REINFORCE Using advantage actor-critic

Actor-critic Training advantage AC

Building a deep deterministic policy gradient

Training DDPG

Exploring trust region policy optimization

Conjugate gradients Trust region methods The TRPO step

Exercises Summary

Optimizing for Continuous Control

Understanding continuous control with Mujoco Introducing proximal policy optimization

The hows of policy optimization PPO and clipped objectives

Using PPO with recurrent networks Deciding on synchronous and asynchronous actors

Using A2C Using A3C

Building actor-critic with experience replay Exercises Summary

All about Rainbow DQN

Rainbow – combining improvements in deep reinforcement learning Using TensorBoard Introducing distributional RL

Back to TensorBoard

Understanding noisy networks

Noisy networks for exploration and importance sampling

Unveiling Rainbow DQN

When does training fail?

Exercises Summary

Exploiting ML-Agents

Installing ML-Agents Building a Unity environment

Building for Gym wrappers

Training a Unity environment with Rainbow Creating a new environment

Coding an agent/environment

Advancing RL with ML-Agents

Curriculum learning Behavioral cloning Curiosity learning Training generalized reinforcement learning agents

Exercises Summary

DRL Frameworks

Choosing a framework Introducing Google Dopamine Playing with Keras-RL Exploring RL Lib Using TF-Agents Exercises Summary

Section 3: Reward Yourself 3D Worlds

Reasoning on 3D worlds Training a visual agent Generalizing 3D vision

ResNet for visual observation encoding

Challenging the Unity Obstacle Tower Challenge

Pre-training the agent Prierarchy – implicit hierarchies

Exploring Habitat – embodied agents by FAIR

Installing Habitat Training in Habitat

Exercises Summary

From DRL to AGI

Learning meta learning

Learning 2 learn Model-agnostic meta learning Training a meta learner

Introducing meta reinforcement learning

MAML-RL

Using hindsight experience replay Imagination and reasoning in RL

Generating imagination

Understanding imagination-augmented agents Exercises Summary

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