> ## Documentation Index
> Fetch the complete documentation index at: https://docs.reinforceui-studio.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Algorithms in ReinforceUI-Studio

ReinforceUI-Studio includes a growing collection of algorithms. This list is continuously updated as new algorithms are added. If you have an algorithm or suggestion, feel free to contact us to have it included.

| **Algorithm** | Paper Link                                                                                 | Citation                                                                                                                                                                                                                                                          |
| ------------- | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| TD3           | [https://arxiv.org/abs/1802.09477v3](https://arxiv.org/abs/1802.09477v3)                   | Fujimoto, Scott, Herke Hoof, and David Meger. "Addressing function approximation error in actor-critic methods." In *International conference on machine learning*, pp. 1587-1596. PMLR, 2018.                                                                    |
| SAC           | [https://arxiv.org/abs/1801.01290](https://arxiv.org/abs/1801.01290)                       | Haarnoja, Tuomas, Aurick Zhou, Pieter Abbeel, and Sergey Levine. "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor." In *International conference on machine learning*, pp. 1861-1870. PMLR, 2018.               |
| TQC           | [https://arxiv.org/abs/2005.04269](https://arxiv.org/abs/2005.04269)                       | Kuznetsov, Arsenii, Pavel Shvechikov, Alexander Grishin, and Dmitry Vetrov. "Controlling overestimation bias with truncated mixture of continuous distributional quantile critics." In *International Conference on Machine Learning*, pp. 5556-5566. PMLR, 2020. |
| DDPG          | [https://arxiv.org/abs/1509.02971](https://arxiv.org/abs/1509.02971)                       | Lillicrap, T. P. "Continuous control with deep reinforcement learning." *arXiv preprint arXiv:1509.02971* (2015).                                                                                                                                                 |
| CTD4          | [https://arxiv.org/abs/2405.02576](https://arxiv.org/abs/2405.02576)                       | Valencia, David, Henry Williams, Yuning Xing,  Trevor Gee, Bruce A. MacDonaland, and Minas Liarokapis. "CTD4-A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics." *arXiv preprint arXiv:2405.02576* (2024).             |
| PPO           | [https://arxiv.org/abs/1707.06347](https://arxiv.org/abs/1707.06347)                       | Schulman, John, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).                                                                                                |
| DQN           | [https://www.nature.com/articles/nature14236](https://www.nature.com/articles/nature14236) | Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves et al. "Human-level control through deep reinforcement learning." nature 518, no. 7540 (2015): 529-533.                                             |