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: [ Ссылка ]
🔗 Deep Deterministic Policy Gradients (DDPG): [ Ссылка ]
🔗 Twin Delayed Deep Deterministic Policy Gradients (TD3): [ Ссылка ]
🔗 Proximal Policy Optimization (PPO): [ Ссылка ]
🔗 Soft Actor Critic (SAC): [ Ссылка ]
🔗 Asynchronous Advantage Actor Critic (A3C): [ Ссылка ]

✏️ Course from Phil Tabor. For more deep reinforcement learning tutorials check out his channel at: [ Ссылка ]


⭐️ 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

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