In DiffClone, we start by selectively sub-sampling trajectories to create a subset of "expert" data. This involves choosing trajectories with the highest rewards, ensuring the dataset captures optimal behaviour. Following this, we employ a Momentum Contrast (MoCo) model, fine-tuned on our datasets, as our visual-encoder backbone. This model processes images to extract relevant states. Once these states are obtained, we normalize them across the dataset to enhance the stability of the policy we intend to learn. Finally, we implement a behaviour cloning agent using a CNN-based Diffusion Policy. We chose this strategy over other offline RL alternatives, motivated by our success in generating an expert dataset that accurately represents the distribution of the given trajectories. The "expert" dataset’s quality and representativeness allowed us to use behaviour cloning techniques that more effectively and accurately replicated the desired optimal behaviour and policy. 

Typical methods in improving behaviour cloning are done by either looking more into the future (increasing horizon length) or by using better sequence modeling architectures such as RNNs, LSTMs etc. Diffusion models have proven successful in capturing complex distributions and efficiently preserving the multi-modality of the distributions they model.