Comments (3)
Totally agree. And while I use TensorFlow almost exclusively now, I have always disliked reading TensorFlow code and found it difficult to settle on a coding style. Until now. TensorFlow should be written as you do.
from reinforcement-learning.
Thanks! Yeah I'll definitely add the missing algorithms, I just have a deadline coming up and didn't have much time the past 2-3 weeks. Should be coming in the next few weeks though!
from reinforcement-learning.
I do agree, Thanks for your keen efforts and well documented repo.
from reinforcement-learning.
Related Issues (20)
- Why CliffWalkingEnv returns 'is_done=True' when reaching cliff? HOT 2
- Is a line missing in 'MC Control with Epsilon-Greedy Policies Solution.ipynb'? HOT 1
- Why is Chapter 11 excluded? HOT 2
- why DQN use kernel size 8 ?
- Gambler's Problem: 0 Stake Allowed?
- Some question in MC Control with Epsilon-Greedy Policies Solution.ipynb HOT 2
- DQL size error
- Policy Evaluation Exercise Solution Is Wrong HOT 1
- Monte Carlo AssertionError: defaultdict(<function mc_control_importance_sampling.<locals>.<lambda> at 0x7f31699ffe18>, {}) (<class 'collections.defaultdict'>)
- Lecture Slides need an update
- Clarification on DQN testing rewards on Atari games
- DQN Testing Rewards on Atari Games HOT 1
- Reinforcement learning policy HOT 1
- Minor Link fix
- A small correction in "MDPs and Bellman Equations" section
- Typo in: "Model-Free Prediction & Control with Monte Carlo (MC)" section -> "Blackjack Playground.ipynb" file:
- Issue in: reinforcement-learning/MC/MC Prediction Solution.ipynb
- please provide requirements.txt or mention the exact version of packages used.
- demystifying-deep-reinforcement-learning link is broken
- MC Control with Epsilon-Greedy Policies ---Epsilon Value and Best Action prob error HOT 1
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from reinforcement-learning.