In this section, we will take a look at the key features that have made the OpenAI Gym toolkit very popular in the reinforcement learning community and led to it becoming widely adopted.
Understanding the features of OpenAI Gym
by Praveen Palanisamy
Hands-On Intelligent Agents with OpenAI Gym
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Contributors
- Preface
- Introduction to Intelligent Agents and Learning Environments
- Reinforcement Learning and Deep Reinforcement Learning
- What is reinforcement learning?
- Understanding what AI means and what's in it in an intuitive way
- Practical reinforcement learning
- Markov Decision Process
- Planning with dynamic programming
- Monte Carlo learning and temporal difference learning
- SARSA and Q-learning
- Deep reinforcement learning
- Practical applications of reinforcement and deep reinforcement learning algorithms
- Summary
- Getting Started with OpenAI Gym and Deep Reinforcement Learning
- Exploring the Gym and its Features
- Implementing your First Learning Agent - Solving the Mountain Car problem
- Understanding the Mountain Car problem
- Implementing a Q-learning agent from scratch
- Training the reinforcement learning agent at the Gym
- Testing and recording the performance of the agent
- A simple and complete Q-Learner implementation for solving the Mountain Car problem
- Summary
- Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
- Improving the Q-learning agent
- Implementing a deep Q-learning agent
- The Atari Gym environment
- Training the deep Q-learner to play Atari games
- Summary
- Creating Custom OpenAI Gym Environments - CARLA Driving Simulator
- Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
- Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
- Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
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Understanding the features of OpenAI Gym
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