Documentation and tutorials¶
One can find more-or-less structured documentation pages on AgentNet functionality here.
AgentNet also has full embedded documentation, so calling
pressing shift+tab in IPython yields a description of object/function.
A standard pipeline of AgentNet experiment is shown in following examples:
- Simple Deep Recurrent Reinforcement Learning setup .
- Most basic demo, if a bit boring. Covers the problem of learning “If X1 than Y1 Else Y2”.
- Uses a single RNN memory and Q-learning algorithm
- Playing Atari SpaceInvaders with Convolutional NN via OpenAI Gym .
- Step-by-step explanation of what you need to do to recreate DeepMind Atari DQN
- Written in a generic way, so that adding recurrent memory or changing learning algorithm could be done in a couple of lines
If you wish to get acquainted with the current library state, view some of the ./examples
- Playing Atari with Convolutional NN via OpenAI Gym
- Can switch to any visual game thanks to awesome Gym interface
- Very simplistic, non-recurrent suffering from atari flickering, etc.
- Deep Recurrent Kung-Fu training with GRUs and actor-critic
- Uses the “Playing atari” example with minor changes
- Trains via Advantage actor-critic (value+policy-based)
- Simple Deep Recurrent Reinforcement Learning setup
- Trying to guess the interconnected hidden factors on a synthetic problem setup
- Stack-augmented GRU generator
- Reproducing http://arxiv.org/abs/1503.01007 with less code
- MOAR deep recurrent value-based LR for wikipedia facts guessing
- Trying to figure a policy on guessing musician attributes (genres, decades active, instruments, etc)
- Using several hidden layers and 3-step Q-learning
- More to come
AgentNet is under active construction, so expect things to change. If you wish to join the development, we’d be happy to accept your help.