A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
lagom is a 'magic' word in Swedish, inte för mycket och inte för lite, enkelhet är bäst (not too much and not too little, simplicity is often the best). It is the philosophy on which this library was designed.
Why to use lagom ?
lagom balances between the flexibility and the usability when developing reinforcement learning (RL) algorithms. The library is built on top of PyTorch and provides modular tools to quickly prototype RL algorithms. However, it does not go overboard, because too low level is often time consuming and prone to potential bugs, while too high level degrades the flexibility which makes it difficult to try out some crazy ideas fast.
We are continuously making lagom more 'self-contained' to set up and run experiments quickly. It internally supports base classes for multiprocessing (master-worker framework) for parallelization (e.g. experiments and evolution strategies). It also supports hyperparameter search by defining configurations either as grid search or random search.