If you’re looking to dive into the world of Deep Reinforcement Learning (DRL), you’ve landed in the right place! This guide will take you through a curated list of essential resources, including libraries, environments, competitions, and much more, all designed to accelerate your learning journey in DRL.

Contents

• Libraries
• Benchmark Results
• Environments
• Competitions
• Timeline
• Books
• Tutorials
• Blogs

Libraries

A plethora of libraries are at your disposal for experimenting with DRL algorithms. Here’s a selection to get started:

• Berkeley Ray RLLib – A scalable open-source library providing a unified API for various reinforcement learning applications.
• Berkeley Softlearning – Focuses on maximum entropy policies for continuous domains.
• Catalyst – Accelerated deep learning for reinforcement learning.
• DeepMind Acme – A research framework aimed at simplifying RL research.
• OpenAI Baselines – High-quality implementations of various reinforcement learning algorithms.

Benchmark Results

Understanding how algorithms perform against each other is crucial. Here are some valuable benchmarking resources:

• DeepMind bsuite
• OpenAI Baselines Results
• OpenAI Spinning Up Benchmark
• SLM Lab Benchmark

Environments

The environments in which your agents learn are just as important as the algorithms themselves. Here are some noteworthy environments:

• AI2-THOR – A photo-realistic interactive framework for AI agents.
• Animal-AI Olympics – Test inspired by animal cognition for AI agents.
• Carla – An open-source autonomous driving research simulator.
• StarCraft II Learning Environment – A platform for reinforcement learning based on StarCraft II.

Competitions

Competitions are a great way to pit your skills against others. Here are some notable ones:

• AWS DeepRacer League 2019
• Flatland Challenge 2019

Timeline

Understanding the evolution of reinforcement learning can give context to its current state. Here’s a brief timeline:

• 1947: Monte Carlo Sampling
• 2015: Deep Q-Networks (DQN) achieves human-level performance in Atari games.
• 2021: General capable agents emerge from open-ended play

Books

To deepen your understanding, check out these insightful books:

• Algorithms for Reinforcement Learning
• An Introduction to Deep Reinforcement Learning

Tutorials

Learning through tutorials can be beneficial for practical understanding. These are highly recommended:

• Andrew Karpathy’s Pong from Pixels
• Berkeley Deep Reinforcement Learning Course

Blogs

Stay updated with the latest trends and insights in DRL by following these blogs:

• Alex Irpan
• DeepMind

Troubleshooting

If you encounter difficulties or have queries while exploring this vast field, consider the following troubleshooting ideas:

• Double-check your library installation and ensure all dependencies are met.
• Consult the GitHub issues page for specific libraries for similar problems and solutions.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.