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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.
Deep Reinforcement Learning with pytorch & visdom
| Date | Stars |
|---|---|
| 2026-07-31 | 802 |
| 2026-08-01 | 802 |
| 2026-08-02 | 802 |
| 2026-08-06 | 802 |
Today
— stars today
This week
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Momentum
0.0
growth rate 0.00%/day
# **Deep Reinforcement Learning** with
# **pytorch** & **visdom**
*******
* Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A3C on InvertedPendulum(MuJoCo)):
<table>
<tr>
<td><img src="/assets/breakout.gif?raw=true" width="200"></td>
<td><img src="/assets/a3c_pong.gif?raw=true" width="200"></td>
<td><img src="/assets/cartpole.gif?raw=true" width="200"></td>
<td><img src="/assets/a3c_con.gif?raw=true" width="200"></td>
</tr>
</table>
* Sample on-line plotting while training an A3C agent on Pong (with 16 learner processes):
* Sample loggings while training a DQN agent on CartPole (we use ```WARNING``` as the logging level currently to get rid of the ```INFO``` printouts from visdom):
```bash
[WARNING ] (MainProcess) <===================================>
[WARNING ] (MainProcess) bash$: python -m visdom.server
[WARNING ] (MainProcess) http://localhost:8097/env/daim_17040900
[WARNING ] (MainProcess) <===================================> DQN
[WARNING ] (MainProcess) <-----------------------------------> Env
[WARNING ] (MainProcess) Creating {gym | CartPole-v0} w/ Seed: 123
[INFO ] (MainProcess) Making new env: CartPole-v0
[WARNING ] (MainProcess) Action Space: [0, 1]
[WARNING ] (MainProcess) State Space: 4
[WARNING ] (MainProcess) <-----------------------------------> Model
[WARNING ] (MainProcess) MlpModel (
(fc1): Linear (4 -> 16)
(rl1): ReLU ()
(fc2): Linear (16 -> 16)
(rl2): ReLU ()
(fc3): Linear (16 -> 16)
(rl3): ReLU ()
(fc4): Linear (16 -> 2)
)
[WARNING ] (MainProcess) No Pretrained Model. Will Train From Scratch.
[WARNING ] (MainProcess) <===================================> Training ...
[WARNING ] (MainProcess) Validation Data @ Step: 501
[WARNING ] (MainProcess) Start Training @ Step: 501
[WARNING ] (MainProcess) Reporting @ Step: 2500 | Elapsed Time: 5.32397913933
[WARNING ] (MainProcess) Training Stats: epsilon: 0.972
[WARNING ] (MainProcess) Training Stats: total_reward: 2500.0
[WARNING ] (MainProcess) Training Stats: avg_reward: 21.7391304348
[WARNING ] (MainProcess) Training Stats: nepisodes: 115
[WARNING ] (MainProcess) Training Stats: nepisodes_solved: 114
[WARNING ] (MainProcess) Training Stats: repisodes_solved: 0.991304347826
[WARNING ] (MainProcess) Evaluating @ Step: 2500
[WARNING ] (MainProcess) Iteration: 2500; v_avg: 1.73136949539
[WARNING ] (MainProcess) Iteration: 2500; tderr_avg: 0.0964358523488
[WARNING ] (MainProcess) Iteration: 2500; steps_avg: 9.34579439252
[WARNING ] (MainProcess) Iteration: 2500; steps_std: 0.798395631184
[WARNING ] (MainProcess) Iteration: 2500; reward_avg: 9.34579439252
[WARNING ] (MainProcess) Iteration: 2500; reward_std: 0.798395631184
[WARNING ] (MainProcess) Iteration: 2500; nepisodes: 107
[WARNING ] (MainProcess) Iteration: 2500; nepisodes_solved: 106
[WARNING ] (MainProcess) Iteration: 2500; repisodes_solved: 0.990654205607
[WARNING ] (MainProcess) Saving Model @ Step: 2500: /home/zhang/ws/17_ws/pytorch-rl/models/daim_17040900.pth ...
[WARNING ] (MainProcess) Saved Model @ Step: 2500: /home/zhang/ws/17_ws/pytorch-rl/models/daim_17040900.pth.
[WARNING ] (MainProcess) Resume Training @ Step: 2500
...
```
*******
## What is included?
This repo currently contains the following agents:
- Deep Q Learning (DQN) [[1]](http://arxiv.org/abs/1312.5602), [[2]](http://home.uchicago.edu/~arij/journalclub/papers/2015_Mnih_et_al.pdf)
- Double DQN [[3]](http://arxiv.org/abs/1509.06461)
- Dueling network DQN (Dueling DQN) [[4]](https://arxiv.org/abs/1511.06581)
- Asynchronous Advantage Actor-Critic (A3C) (w/ both discrete/continuous action space support) [[5]](https://arxiv.org/abs/1602.01783), [[6]](https://arxiv.org/abs/1506.02438)
- Sample Efficient Actor-Critic with Experience Replay (ACER) (currently w/ discrete action space support (Truncated Importance Sampling, 1st Order TRPO)) [[7]](https://arxiExcerpt of 8,684 characters
Read on GitHub
66
15
Praveen Palanisamy · @microsoft
4
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:797b9100409e7416, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning
matched fp:797b9100409e7416, topic:deep-learning, topic:pytorch