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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Python Implementation of Reinforcement Learning: An Introduction
| Date | Stars |
|---|---|
| 2026-07-24 | 14729 |
| 2026-07-25 | 14727 |
| 2026-07-28 | 14727 |
| 2026-07-30 | 14727 |
| 2026-07-31 | 14734 |
| 2026-08-01 | 14734 |
| 2026-08-02 | 14735 |
| 2026-08-05 | 14738 |
| 2026-08-06 | 14738 |
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# Reinforcement Learning: An Introduction [](https://travis-ci.org/ShangtongZhang/reinforcement-learning-an-introduction) Python replication for Sutton & Barto's book [*Reinforcement Learning: An Introduction (2nd Edition)*](http://incompleteideas.net/book/the-book-2nd.html) > If you have any confusion about the code or want to report a bug, please open an issue instead of emailing me directly, and unfortunately I do not have exercise answers for the book. # Contents ### Chapter 1 1. Tic-Tac-Toe ### Chapter 2 1. [Figure 2.1: An exemplary bandit problem from the 10-armed testbed](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_2_1.png) 2. [Figure 2.2: Average performance of epsilon-greedy action-value methods on the 10-armed testbed](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_2_2.png) 3. [Figure 2.3: Optimistic initial action-value estimates](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_2_3.png) 4. [Figure 2.4: Average performance of UCB action selection on the 10-armed testbed](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_2_4.png) 5. [Figure 2.5: Average performance of the gradient bandit algorithm](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_2_5.png) 6. [Figure 2.6: A parameter study of the various bandit algorithms](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_2_6.png) ### Chapter 3 1. [Figure 3.2: Grid example with random policy](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_3_2.png) 2. [Figure 3.5: Optimal solutions to the gridworld example](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_3_5.png) ### Chapter 4 1. [Figure 4.1: Convergence of iterative policy evaluation on a small gridworld](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_4_1.png) 2. [Figure 4.2: Jack’s car rental problem](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_4_2.png) 3. [Figure 4.3: The solution to the gambler’s problem](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_4_3.png) ### Chapter 5 1. [Figure 5.1: Approximate state-value functions for the blackjack policy](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_5_1.png) 2. [Figure 5.2: The optimal policy and state-value function for blackjack found by Monte Carlo ES](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_5_2.png) 3. [Figure 5.3: Weighted importance sampling](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_5_3.png) 4. [Figure 5.4: Ordinary importance sampling with surprisingly unstable estimates](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_5_4.png) ### Chapter 6 1. [Example 6.2: Random walk](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/example_6_2.png) 2. [Figure 6.2: Batch updating](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_6_2.png) 3. [Figure 6.3: Sarsa applied to windy grid world](https://raw.githubusercontent.com/ShangtongZhang/reinforcement-learning-an-introduction/master/images/figure_6_3.png) 4. [Figure 6.4: The cliff
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:93a37882922e0960, topic:reinforcement-learning, name:reinforcement learning, desc:reinforcement learning