Reinforcement Learning Course: Intro to Advanced Actor Critic Methods



Actor critic methods are used in machine learning. They are most useful for applications in robotics as they allow us to output continuous, rather than discrete actions. This enables control of electric motors to actuate movement in robotic systems, at the expense of increased computational complexity. 💻 Code for the algorithms covered:
🔗 Actor Critic: https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/actor_critic/tensorflow2
🔗 Deep Deterministic Policy Gradients (DDPG): https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/DDPG/tensorflow2/pendulum
🔗 Twin Delayed Deep Deterministic Policy Gradients (TD3): https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/TD3/tf2
🔗 Proximal Policy Optimization (PPO): https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/PPO/torch
🔗 Soft Actor Critic (SAC): https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/SAC/tf2
🔗 Asynchronous Advantage Actor Critic (A3C): https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/A3C/pytorch ✏️ Course from Phil Tabor. For more deep reinforcement learning tutorials check out his channel at: https://www.youtube.com/machinelearningwithphil ⭐️ Course Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:04:03) Actor Critic (TF2)
⌨️ (0:44:50) DDPG (TF2)
⌨️ (1:52:36) TD3 (TF2)
⌨️ (3:08:29) PPO (PyTorch)
⌨️ (4:03:16) SAC (TF2)
⌨️ (5:09:28) A3C (PyTorch) ⭐️ Software requirements ⭐️ Python 3.x
box2d-py 2.3.8
gym 0.15.4
matplotlib 3.1.1
numpy 1.18.1
pybullet 2.8.5
torch 1.4.0
tensorflow-gpu 2.3.1 🎉 Thanks to our Champion and Sponsor supporters:
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Reinforcement Learning Course: Intro to Advanced Actor Critic Methods

Reinforcement Learning Course: Intro to Advanced Actor Critic Methods

 

 

 

 

 

 

 

 

 

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