deep-reinforcement-learning-in-python
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2017-5-30 13:16 | 2024-10-31 07:59 | 96 | 482.94 MB | 45 |
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文件列表
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/018 RBF Networks with CartPole Theory.mp43.05MB
- 04 TD Lambda/028 TD Lambda Summary.mp43.64MB
- 02 Background Review/009 Review of Approximation Methods for Reinforcement Learning.mp43.67MB
- 07 Appendix/045 Where to get Udemy coupons and FREE deep learning material.mp44.02MB
- 02 Background Review/004 Review Intro.mp44.19MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/013 Saving a Video.mp44.54MB
- 04 TD Lambda/024 N-Step Methods.mp45.02MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/021 Tensorflow Warmup.mp45.06MB
- 01 Introduction and Logistics/002 Where to get the Code.mp45.19MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/023 OpenAI Gym Section Summary.mp45.31MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/020 Theano Warmup.mp45.83MB
- 06 Deep Q-Learning/036 Deep Q-Learning Intro.mp45.9MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/022 Plugging in a Neural Network.mp45.91MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/014 CartPole with Bins Theory.mp46.02MB
- 02 Background Review/007 Review of Monte Carlo Methods.mp46.18MB
- 05 Policy Gradients/033 Mountain Car Continuous Specifics.mp46.5MB
- 02 Background Review/006 Review of Dynamic Programming.mp46.51MB
- 05 Policy Gradients/032 Continuous Action Spaces.mp46.58MB
- 02 Background Review/008 Review of Temporal Difference Learning.mp47.15MB
- 06 Deep Q-Learning/043 Partially Observable MDPs.mp47.6MB
- 04 TD Lambda/027 TD Lambda in Code.mp47.62MB
- 06 Deep Q-Learning/040 Additional Implementation Details for Atari.mp48.51MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/011 OpenAI Gym Tutorial.mp48.67MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/019 RBF Networks with CartPole Code.mp48.91MB
- 04 TD Lambda/025 N-Step in Code.mp49.47MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/012 Random Search.mp410.29MB
- 02 Background Review/010 Review of Deep Learning.mp411.04MB
- 04 TD Lambda/026 TD Lambda.mp411.77MB
- 02 Background Review/005 Review of Markov Decision Processes.mp412.25MB
- 05 Policy Gradients/031 Policy Gradient in Theano for CartPole.mp413.44MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/017 RBF Networks with Mountain Car Code.mp413.75MB
- 06 Deep Q-Learning/039 Deep Q-Learning in Theano for CartPole.mp413.76MB
- 01 Introduction and Logistics/003 How to Succeed in this Course.mp414.37MB
- 06 Deep Q-Learning/037 Deep Q-Learning Techniques.mp414.44MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/015 CartPole with Bins Code.mp414.7MB
- 06 Deep Q-Learning/038 Deep Q-Learning in Tensorflow for CartPole.mp414.98MB
- 06 Deep Q-Learning/041 Deep Q-Learning in Tensorflow for Breakout.mp415.76MB
- 01 Introduction and Logistics/001 Introduction and Outline.mp415.83MB
- 03 OpenAI Gym and Basic Reinforcement Learning Techniques/016 RBF Neural Networks.mp416.51MB
- 05 Policy Gradients/029 Policy Gradient Methods.mp417.94MB
- 05 Policy Gradients/030 Policy Gradient in TensorFlow for CartPole.mp417.97MB
- 05 Policy Gradients/034 Mountain Car Continuous Theano.mp419.06MB
- 06 Deep Q-Learning/042 Deep Q-Learning in Theano for Breakout.mp420.03MB
- 05 Policy Gradients/035 Mountain Car Continuous Tensorflow.mp420.09MB
- 07 Appendix/044 Environment Setup.mp443.92MB
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