**MushroomRL** is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. The architecture of MushroomRL is built in such a way that every component of an RL problem is already provided, and most of the time users can only focus on the implementation of their own algorithms and experiments. MushroomRL comes with a strongly modular architecture that makes it easy to understand how each component is structured and how it interacts with other ones; moreover it provides an exhaustive list of RL methodologies, such as:

A novel built-in attention mechanism, that is complementary to all other prior attention mechanisms (e.g. squeeze and excitation, transformers) that are external (i.e., not built-in - please read paper for more details)

Weight excitation

Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks

MushroomRL

MushroomRL: Simplifying Reinforcement Learning Research

**XLM** is a [Transformer](https://paperswithcode.com/method/transformer) based architecture that is pre-trained using one of three language modelling objectives:

1. Causal Language Modeling - models the probability of a word given the previous words in a sentence.
2. Masked Language Modeling - the masked language modeling objective of [BERT](https://paperswithcode.com/method/bert).
3. Translation Language Modeling - a (new) translation language modeling objective for improving cross-lingual pre-training.

The authors find that both the CLM and MLM approaches provide strong cross-lingual features that can be used for pretraining models.

Source	MushroomRL: Simplifying Reinforcement Learning Research
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com