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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.
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.
TensorFlow2 Reinforcement Learning
| Date | Stars |
|---|---|
| 2026-07-24 | 473 |
| 2026-07-25 | 473 |
| 2026-07-28 | 473 |
| 2026-07-30 | 473 |
| 2026-07-31 | 473 |
| 2026-08-06 | 473 |
Today
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Momentum
0.0
growth rate 0.00%/day
[](https://github.com/keiohta/tf2rl/actions/workflows/test.yml) [](https://coveralls.io/github/keiohta/tf2rl?branch=master) [](LICENSE) []() [](https://badge.fury.io/py/tf2rl) # TF2RL TF2RL is a deep reinforcement learning library that implements various deep reinforcement learning algorithms using [TensorFlow 2.x](https://www.tensorflow.org/). ## 1. Algorithms Following algorithms are supported: | Algorithm | Dicrete action | Continuous action | Support | Category | | :----------------------------------------------------------: | :------------: | :---------------: | :----------------------------------------: | ------------------------ | | [VPG](https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf), [PPO](<https://arxiv.org/abs/1707.06347>) | ✓ | ✓ | [GAE](https://arxiv.org/abs/1506.02438) | Model-free On-policy RL | | [DQN](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) (including [DDQN](https://arxiv.org/abs/1509.06461), [Prior. DQN](https://arxiv.org/abs/1511.05952), [Duel. DQN](https://arxiv.org/abs/1511.06581), [Distrib. DQN](<https://arxiv.org/abs/1707.06887>), [Noisy DQN](<https://arxiv.org/abs/1706.10295>)) | ✓ | - | [ApeX](<https://arxiv.org/abs/1803.00933>) | Model-free Off-policy RL | | [DDPG](https://arxiv.org/abs/1509.02971) (including [TD3](<https://arxiv.org/abs/1802.09477>), [BiResDDPG](<https://arxiv.org/abs/1905.01072>)) | - | ✓ | [ApeX](<https://arxiv.org/abs/1803.00933>) | Model-free Off-policy RL | | [SAC](<https://arxiv.org/abs/1801.01290>) | ✓ | ✓ | [ApeX](<https://arxiv.org/abs/1803.00933>) | Model-free Off-policy RL | | [CURL](https://arxiv.org/abs/2004.04136), [SAC-AE](https://arxiv.org/abs/1910.01741) | - | ✓ | - | Model-free Off-policy RL | | [MPC](https://arxiv.org/abs/1708.02596), [ME-TRPO](https://arxiv.org/abs/1802.10592) | ✓ | ✓ | - | Model-base RL | | [GAIL](<https://arxiv.org/abs/1606.03476>), [GAIfO](<https://arxiv.org/abs/1807.06158>), [VAIL](<https://arxiv.org/abs/1810.00821>) (including [Spectral Normalization](<https://arxiv.org/abs/1802.05957>)) | ✓ | ✓ | - | Imitation Learning | Following papers have been implemented in tf2rl: - Model-free On-policy RL - [Policy Gradient Methods for Reinforcement Learning with Function Approximation](https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf), [code](<https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py>) - [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://arxiv.org/abs/1506.02438), [code](<https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/discount_cumsum.py>) - [Proximal Policy Optimization Algorithms](<https://arxiv.org/abs/1707.06347>), [code](<https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ppo.py>) - Model-free Off-policy RL - [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), [code](<https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/dqn.py>) - [Human-level control through Deep Reinforcement Learning](https://storage.google
Excerpt of 13,187 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1f1b628ddd1bdc42, topic:reinforcement-learning, topic:deep-reinforcement-learning, topic:imitation-learning