TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow
summary
Demo of TF-Agents shows a working, open source TensorFlow 2.0 RL stack with concrete code: REINFORCE solves CartPole in ~400 episodes; DQN learns Breakout after about 4–5 million frames using replay buffer and parallel environments; Soft Actor-Critic trains a Minitaur in PyBullet and transfers to hardware after ~13–16k episodes. Components are modular (env specs, drivers, agents, Keras nets), support OpenAI/Atari, and are designed for extensibility and reproducible training with logging and TensorBoard.
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chapters (5)
- 0:00Intro
- 1:49Supervised Learning
- 7:31CartPole Policy Gradients
- 16:45Atari Deep Q-Learning
- 26:10Minitaur Soft Actor Critic
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