Top AI Repos — open-source AI, indexed and scored
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.
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.
Reinforcement learning algorithms implemented for Tensorflow 2.0+ [DQN, DDPG, AE-DDPG, SAC, PPO, Primal-Dual DDPG]
| Date | Stars |
|---|---|
| 2026-07-31 | 316 |
| 2026-08-01 | 316 |
| 2026-08-02 | 316 |
| 2026-08-04 | 316 |
| 2026-08-06 | 316 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Reinforcement Learning Agents Implemented for Tensorflow 2.0+ ## New Updates! - DDPG with prioritized replay - Primal-Dual DDPG for CMDP ## Future Plans - SAC Discrete ## Usage - Install dependancies imported ([my tf2 conda env as reference](https://github.com/anita-hu/TF2-RL/blob/master/mytf2env.txt)) - Each file contains example code that runs training on CartPole env - Training: `python3 TF2_DDPG_LSTM.py` - Tensorboard: `tensorboard --logdir=DDPG/logs` ## Hyperparameter tuning - Install hyperopt https://github.com/hyperopt/hyperopt - Optional: switch agent used and configure param space in `hyperparam_tune.py` - Run: `python3 hyperparam_tune.py` ## Agents Agents tested using CartPole env. | Name | On/off policy | Model | Action space support | | --- | --- | --- | --- | | [DQN](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | off-policy | Dense, LSTM | discrete | | [DDPG](https://arxiv.org/pdf/1509.02971.pdf) | off-policy | Dense, LSTM | discrete, continuous | | [AE-DDPG](https://arxiv.org/pdf/1903.00827.pdf) | off-policy | Dense | discrete, continuous | | [SAC:bug:](https://arxiv.org/pdf/1812.05905.pdf) | off-policy | Dense | continuous | | [PPO](https://arxiv.org/pdf/1707.06347.pdf) | on-policy | Dense | discrete, continuous | #### Contrained MDP | Name | On/off policy | Model | Action space support | | --- | --- | --- | --- | | [Primal-Dual DDPG](https://arxiv.org/pdf/1802.06480.pdf) | off-policy | Dense | discrete, continuous| ## Models Models used to generate the demos are included in the repo, you can also find q value, reward and/or loss graphs ## Demos | DQN Basic, time step = 4, 500 reward | DQN LSTM, time step = 4, 500 reward | | --- | --- | | <img src="DQN/gifs/test_render_basic_time_step4_reward500.gif" height="200"> | <img src="DQN/gifs/test_render_lstm_time_step4_reward500.gif" height="200"> | | DDPG Basic, 500 reward | DDPG LSTM, time step = 5, 500 reward | | --- | --- | | <img src="DDPG/gifs/test_render_basic_reward500.gif" height="200"> | <img src="DDPG/gifs/test_render_lstm_time_step5_reward500.gif" height="200"> | | AE-DDPG Basic, 500 reward | PPO Basic, 500 reward | | --- | -- | | <img src="AE-DDPG/gifs/test_render_basic_reward500.gif" height="200"> | <img src="PPO/gifs/test_render_basic_reward500.gif" height="200"> |
Excerpt of 2,302 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fe76b0b719a7d704, topic:reinforcement-learning, topic:openai-gym, desc:reinforcement learning