Book Image

Deep Reinforcement Learning with Python - Second Edition

By : Sudharsan Ravichandiran
Book Image

Deep Reinforcement Learning with Python - Second Edition

By: Sudharsan Ravichandiran

Overview of this book

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.
Table of Contents (22 chapters)
18
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19
Index

Lunar lander using A2C

Let's learn how to implement A2C with Stable Baselines for the lunar landing task. In the lunar lander environment, our agent drives the space vehicle, and the goal of the agent is to land correctly on the landing pad. If our agent (lander) lands away from the landing pad, then it loses the reward, and the episode will get terminated if the agent crashes or comes to rest. The action space of the environment includes four discrete actions, which are: do nothing, fire left orientation engine, fire main engine, and fire right orientation engine. Now, let's see how to train the agent using A2C to correctly land on the landing pad.

First, let's import the necessary libraries:

import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines.common.evaluation import evaluate_policy
from stable_baselines import A2C

Create the lunar lander environment using...