Part 1: Key Concepts in RL — Spinning Up documentation
In a nutshell, RL is the study of agents and how they learn by trial and error. It formalizes the idea that rewarding or punishing an agent for its behavior makes it more likely to repeat or forego that behavior in the future.
Part 1: Key Concepts in RL - Spinning Up documentation --> Docs >> Part 1: Key Concepts in RL Edit on GitHub Part 1: Key Concepts in RL ¶ Table of Contents Part 1: Key Concepts in RL What Can RL Do? Key Concepts and Terminology (Optional) Formalism Welcome to our introduction to reinforcement learning! Here, we aim to acquaint you with the language and notation used to discuss the subject, a high-level explanation of what RL algorithms do (although we mostly avoid the question of how they do it), and a little bit of the core math that underlies the algorithms. In a nutshell, RL is the study of
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related reading
- Part 1: Key Concepts in RL - Spinning Up documentationspinningup.openai.com
- A (Long) Peek into Reinforcement Learning | Lil'Loglilianweng.github.io
- Part 2: Kinds of RL Algorithms - Spinning Up documentationspinningup.openai.com
- Deep Reinforcement Learning: Pong from Pixelskarpathy.github.io
- Reward is not the optimization target — LessWronglesswrong.com
- An Updated Introduction to Reinforcement Learning | Sri's Blogsrianumakonda.com
- Part 3: Intro to Policy Optimization - Spinning Up documentationspinningup.openai.com
- Reinforcement learning - Wikipediaen.wikipedia.org
- RL_Notes__final_.pdfjubayer-ibn-hamid.github.io
- [1912.01683] Optimal Policies Tend to Seek Powerarxiv.org
- Harsh Bhatt (@harshbhatt7585) on Xx.com
- Key Papers in Deep RL - Spinning Up documentationspinningup.openai.com