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.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
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

About

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 …

reinforcement-learning deep-q-network actor-critic deep-deterministic-policy-gradient reinforcement-learning-agent