AgentNet is a toolkit for Deep Reinforcement Learning agent design and training.
The core idea is to merge all the newest neural network layers and tools from Lasagne and Theano with Reinforcement Learning formulation and algorithms. The primary goal - make it easy and intuitive to fuse arbitrary neural network architectures into the world of reinforcement learning.
All techno-babble set aside, you can use AgentNet to __train your pet neural network to play games!__ [e.g. Atari, Doom] in a single notebook.
AgentNet has full in-and-out support for __Lasagne__ deep learning library, granting you access to all convolutions, maxouts, poolings, dropouts, etc. etc. etc.
AgentNet handles both discrete and continuous control problems and supports arbirary recurrent agent mempory structure. It also has an [experimental] support for hierarchical reinforcement learning.
- The library implements numerous reinforcement learning algorithms including
- Q-learning (or deep Q-learning, since we support arbitrary complexity of network)
- N-step Q-learning
- N-step Advantage Actor-Critic (A2c)
- N-step Deterministic Policy Gradient (DPG)
As a side-quest, we also provide a boilerplate to custom long-term memory network architectures (see examples).
AgentNet is a library designed to create and evaluate deep reinforcement learning agents. The library is optimized for ease of prototyping and
- Agent and Recurrence
- Memory layers
- Learning Algorithms
- Target Network