RLAlgsInMDPs.pdf
1.5 MB
Algorithms for Reinforcement Learning
Csaba Szepesv´ari
Csaba Szepesv´ari
اگه خواستین کد پایتونتون رو زیبا بزنید (یا کدای زیبای پایتونو راحتتر بخونید) اینجا منبع خوبیه:
https://www.python.org/dev/peps/pep-0008/
https://www.python.org/dev/peps/pep-0008/
Python Enhancement Proposals (PEPs)
PEP 8 – Style Guide for Python Code | peps.python.org
This document gives coding conventions for the Python code comprising the standard library in the main Python distribution. Please see the companion informational PEP describing style guidelines for the C code in the C implementation of Python.
1701.07274v1.pdf
410.9 KB
DEEP REINFORCEMENT LEARNING: AN OVERVIEW
Practical Reinforcement Learning
https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
GitHub
GitHub - gordicaleksa/pytorch-learn-reinforcement-learning: A collection of various RL algorithms like policy gradients, DQN and…
A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL probl...
Forwarded from Deep Learning Assets
Forbes
Reinforcement Learning: The Next Big Thing For AI (Artificial Intelligence)?
It is already showing lots of promise
Share some interview questions when I applied a RL engineer of a game company (NetEase Inc. Hangzhou China):
1. The benefits of target network and replay buffer in DQN.
2. Introduce the A3C and the differences with A2C.
3. Discuss the difficulties about learning an agent for LOL or Dota.
4. How to deal with sparse reward?
5. Discuss the adjustment of discount factor.
6. Introduce some enhancement of DQN like Double-DQN.
1. The benefits of target network and replay buffer in DQN.
2. Introduce the A3C and the differences with A2C.
3. Discuss the difficulties about learning an agent for LOL or Dota.
4. How to deal with sparse reward?
5. Discuss the adjustment of discount factor.
6. Introduce some enhancement of DQN like Double-DQN.