PyBrain's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.

PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library.

While there are a few machine learning libraries out there, PyBrain aims to be a very easy-to-use modular library that can be used by entry-level students but still offers the flexibility and algorithms for state-of-the-art research.

PyBrain, as it's written-out name already suggests, contains algorithms for neural networks, for reinforcement learning (and the combination of the two), for unsupervised learning, and evolution.

Since most of the current problems deal with action spaces and continuous state, function approximators (like neural networks) have to be used to cope with the large dimensionality.

The PyBrain library is built around neural networks in the kernel and all of the training methods accept a neural network as the to-be-trained instance. This makes PyBrain a powerful tool for real-life tasks.

Here are some key features of "PyBrain":

· Backprop
· Rprop
· Policy Gradients
· Support Vector Machines
· Evolution Strategies
· CMA-ES
· Competitive Coevolution
· Natural ES
· Natural Actor-Critic
· Reward-weighted regression
· Evolino
· Fitness Expectation Maximization
· SPLA
· PCA/pPCA
· LSH for Hamming and Euclidean Spaces