course-for-free.-com-udemy-cutting-edge-ai-deep-reinforcement-learning-in-python

Creation Time	Dec. 24, 2025, 6:01 a.m.
Last Access Time	Jan. 10, 2026, 9:13 p.m.
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Keywords	course-for-free -com-udemy-cutting-edge-ai-deep-reinforcement-learning-in-python
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6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 194.3 MB
4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).mp4 193.6 MB
3. A2C (Advantage Actor-Critic)/10. A2C.mp4 192.3 MB
6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 167.0 MB
5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.mp4 142.2 MB
3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.mp4 128.6 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 123.0 MB
6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 117.5 MB
4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.mp4 110.5 MB
2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).mp4 108.7 MB
5. ES (Evolution Strategies)/2. ES Theory.mp4 108.2 MB
3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).mp4 96.2 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 88.4 MB
6. Appendix FAQ/6. How to Code by Yourself (part 1).mp4 82.6 MB
4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.mp4 80.7 MB
2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).mp4 78.6 MB
6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.mp4 71.6 MB
3. A2C (Advantage Actor-Critic)/7. Multiple Processes.mp4 70.1 MB
5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.mp4 68.6 MB
4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).mp4 64.8 MB
3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.mp4 61.3 MB
5. ES (Evolution Strategies)/6. Flappy Bird.mp4 60.9 MB
6. Appendix FAQ/7. How to Code by Yourself (part 2).mp4 56.7 MB
5. ES (Evolution Strategies)/5. ES for Supervised Learning.mp4 55.2 MB
5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.mp4 53.1 MB
1. Welcome/2. Outline.mp4 51.2 MB
2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.mp4 49.7 MB
5. ES (Evolution Strategies)/4. ES for Optimizing a Function.mp4 46.5 MB
3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.mp4 45.7 MB
4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.mp4 45.2 MB
5. ES (Evolution Strategies)/1. ES Section Introduction.mp4 44.9 MB
6. Appendix FAQ/5. How to Succeed in this Course (Long Version).mp4 39.3 MB
1. Welcome/1. Introduction.mp4 35.8 MB
3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.mp4 32.7 MB
3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).mp4 32.6 MB
2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.mp4 32.1 MB
2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.mp4 31.2 MB
5. ES (Evolution Strategies)/9. ES Section Summary.mp4 28.6 MB
3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.mp4 28.5 MB
3. A2C (Advantage Actor-Critic)/5. A2C Demo.mp4 27.4 MB
1. Welcome/3. Where to get the code.mp4 24.5 MB
4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.mp4 23.9 MB
6. Appendix FAQ/9. Python 2 vs Python 3.mp4 19.0 MB
2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.mp4 18.9 MB
6. Appendix FAQ/1. What is the Appendix.mp4 18.1 MB
4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.mp4 17.6 MB
3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).mp4 14.2 MB
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CourseForFree.Com-Udemy-Cutting-Edge-AI-Deep-Reinforcement-Learning-in-Python.torrent 75.7 KB
6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.7 KB
3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).vtt 22.8 KB
5. ES (Evolution Strategies)/2. ES Theory.vtt 22.4 KB
2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).vtt 22.3 KB
4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.vtt 21.1 KB
6. Appendix FAQ/11. What order should I take your courses in (part 2).vtt 20.2 KB
4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.vtt 20.0 KB
4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).vtt 20.0 KB
3. A2C (Advantage Actor-Critic)/10. A2C.vtt 19.7 KB
6. Appendix FAQ/6. How to Code by Yourself (part 1).vtt 19.4 KB
2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).vtt 18.6 KB
6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.vtt 17.3 KB
2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.vtt 15.5 KB
5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.vtt 15.5 KB
6. Appendix FAQ/10. What order should I take your courses in (part 1).vtt 14.2 KB
5. ES (Evolution Strategies)/6. Flappy Bird.vtt 13.8 KB
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6. Appendix FAQ/5. How to Succeed in this Course (Long Version).vtt 12.8 KB
6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.6 KB
6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.vtt 12.3 KB
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4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.vtt 10.5 KB
5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.vtt 10.1 KB
3. A2C (Advantage Actor-Critic)/7. Multiple Processes.vtt 9.6 KB
1. Welcome/2. Outline.vtt 9.3 KB
3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.vtt 9.2 KB
2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.vtt 8.5 KB
2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.vtt 8.3 KB
3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.vtt 8.1 KB
5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.vtt 8.1 KB
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3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.vtt 7.8 KB
5. ES (Evolution Strategies)/1. ES Section Introduction.vtt 7.5 KB
2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.vtt 7.2 KB
5. ES (Evolution Strategies)/4. ES for Optimizing a Function.vtt 6.8 KB
5. ES (Evolution Strategies)/5. ES for Supervised Learning.vtt 6.7 KB
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