This utility method returns the number of frames (or environment steps) executed per actual (wall-clock) second. This is useful to keep track of the environment execution speeds and how fast the agent can sample from the environment.
- 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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