GitHub - MZayed47/DDPG_Agent: Deep Deterministic Policy Gradient (DDPG) is a model-free off-policy algorithm for learning continuous actions. It is a reinforcement learning technique that combines ideas from DPG (Deterministic Policy Gradient) and DQN (Deep Q-Network). From DQN, it uses Experience Replay and Slow-learning target networks. From DPG, it incorporates Operating over continuous action spaces.

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Deep Deterministic Policy Gradient (DDPG) is a model-free off-policy algorithm for learning continuous actions. It is a reinforcement learning technique that combines ideas from DPG (Deterministic Policy Gradient) and DQN (Deep Q-Network). From DQN, it uses Experience Replay and Slow-learning target networks. From DPG, it incorporates Operating …

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Name	Name	Last commit message	Last commit date
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Articles	Articles
Presentations	Presentations
DDPG_Scratch.ipynb	DDPG_Scratch.ipynb
DDPG_pendulum.ipynb	DDPG_pendulum.ipynb
Links - DDPG.txt	Links - DDPG.txt
buffer.py	buffer.py
pendulum.mp4	pendulum.mp4