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Inside TensorFlow - TF-Agents
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- 1 Intro
- 2 Supervised learning is not enough
- 3 Differences with supervised learning
- 4 Typical supervised learning
- 5 Typical reinforcement learning
- 6 Measuring the reliability of RL algorithms
- 7 Playing Breakout faster in TF
- 8 Learnable policy
- 9 Policy Saver
- 10 Available agents
- 11 TF-Agents distributed collection
- 12 Distributed architecture
- 13 Bandits vs RL
- 14 A/B testing
- 15 Multi-armed bandits
- 16 Bandit agents
- 17 Recommender systems
- 18 TF-Agents: Roadmap
- 19 TF-Agents developer investment
