update README

README.md

@@ -27,7 +27,7 @@ CityLearn may still work with some earlier versions of these libraries, but we h
 - [building_attributes.json](/data/building_attributes.json): json file containing the attributes of the buildings and which users can modify.
 - [citylearn.py](/citylearn.py): Contains the ```CityLearn``` environment and the functions ```building_loader()``` and ```autosize()```
 - [energy_models.py](/energy_models.py): Contains the classes ```Building```, ```HeatPump``` and ```EnergyStorage```, which are called by the ```CityLearn``` class.
-- [agent.py](/agent.py): Implementation of the TD3 algorithm ([MARLISA](https://www.researchgate.net/publication/344502330_MARLISA_Multi-Agent_Reinforcement_Learning_with_Iterative_Sequential_Action_Selection_for_Load_Shaping_of_Grid-Interactive_Connected_Buildings)) RL algorithm, based on [SAC](https://arxiv.org/abs/1812.05905). This file must be modified with any other RL implementation, which can then be run in the [main.ipynb](/main.ipynb) jupyter lab file or the [main.py](/main.py) file.
+- [agent.py](/agent.py): Implementation of our MARLISA algorithm ([MARLISA](https://dl.acm.org/doi/10.1145/3408308.3427604)) RL algorithm, based on [SAC](https://arxiv.org/abs/1812.05905). This file must be modified with any other RL implementation, which can then be run in the [main.ipynb](/main.ipynb) jupyter lab file or the [main.py](/main.py) file. Checkout our presentation video [here](https://www.youtube.com/watch?v=897ms6DrZjo)!
 - [reward_function.py](/reward_function.py): Contains the reward functions that wrap and modifiy the rewards obtained from ```CityLearn```. This function can be modified by the user in order to minimize the cost function of ```CityLearn```. There are two reward functions, one works for multi-agent systems (decentralized RL agents), and the other works for single-agent systems (centralized RL agent). Setting the attribute central_agent=True in CityLearn will make the environment return the output from sa_reward_function, while central_agent=False (default mode) will make the environment return the output from ma_reward_function.
 - [example_rbc.ipynb](/example_rbc.ipynb): jupyter lab file. Example of the implementation of a manually optimized Rule-based controller (RBC) that can be used for comparison
 - [example_central_agent.ipynb](/example_central_agent.ipynb): jupyter lab file. Example of the implementation of a SAC centralized RL algorithm from Open AI stable baselines, for 1 and 9 buildings.