Update README.md

Author: team-daniel

README.md

@@ -10,7 +10,11 @@ Safe exploration of reinforcement learning (RL) agents during training is a crit
 <p align="center">Fig 1. A high-level overview of ADVICE including training, inference, and the adaptive extension.</p>
 
 ### ADVICE
-Add short methodology here...
+ ADVICE starts with collecting a dataset of state-action pairs, classified as either safe or unsafe based on the outcomes they lead to within the training environment. This dataset is then used to train the contrastive autoencoder. The training process leverages a unique loss function that helps the model learn by comparing similar (safe) and dissimilar (unsafe) pairs, enhancing its ability to identify and categorize new observations quickly. To classify unseen data, a nearest neighbours model is fit on the final embedding space.
+ 
+ Once trained, ADVICE operates by passing the current state and the desired action through the encoder and then classifying the unseen embeddings using the nearest neighbours model and a safety threshold k. If deemed safe, the RL agent can continue. If deemed unsafe, ADVICE will select the next best safe action. The paramter k denotes the conservativeness of the shield.
+ 
+ The adaptive nearest neighbours module automatically adjusts the conservativeness of the safety threshold dynamically, based on the agent's recent performance. If the agent has been performing safely, the system can allow more exploratory actions; conversely, if safety violations increase, the system becomes more conservative.
 
 ### Getting Started
 To get started you can run the `main.py` file from our source code. To run ADVICE in the [safety gymnasium](https://github.com/PKU-Alignment/safety-gymnasium) test suite, it is required you run the following pip command: