Hello!

As some of you may know, I am really into AI, especially reinforcement learning.

I use SFML to set up experimentation environments and to visualize them.

So, I thought I should post here about what is now a rather large collection of reinforcement learning agents.

I am still using the same cart-pole balancing simulation and mountain car problems, but I recently also added a maze.

Perhaps some of you find it useful for a game or research!

Link to repository:

https://github.com/222464/AILibHere is the list of available agents and components of the agents, as of 9/23/2014:

- APSSC - ant colony optimization based labeler
- Evolved CTRNN - a second-order recursive neural network used for evolution
- ConvNet - 2D convolutional neural network for feature extraction using restricted boltzmann machiness
- Autoencoder - unsupervised learning and feature compression
- DBN - deep belief network (stacked RBM)
- DSOM - deep self-organizing map for massive feature reduction
- FA- function approximator (standard multi-layer perceptron)
- FERL - free-energy based reinforcement learner. Simplest learner in this library
- RBM - generic restricted boltzmann machine
- DNF - dynamic neural field reinforcement learner (hebbian based)
- Elman - simple Elman network implementation
- TDFalcon - ART (adaptive resonance theory) based reinforcement learning
- HTM - hierarchical temporal memory
- HTMRL - continuous action and state POMDP reinforcement learner
- HTMRLDiscreteAction - discrete action and continuous state POMDP reinforcement learner
- Bayesian Optimizer - simple implementation
- HyperNet - proto-ERL
- LSTMG - generalized LSTM implementation
- LSTMActorCritic - actor-critic using LSTMG
- LSTMRL- simpler version of LSTM discrete action reinforcement learner
- ActorCriticAgent - backprop critic, hebbian actor
- CACLA - continuous actor-critic learning automaton
- MemoryActor - actor portion only with hebbian learning and memory cells
- MultiQ - simple Q learner with function approximation and discrete actions
- NCPSOAgent - no critic particle swarm optimization agent
- PSOAgent - critic included particle swarm optimization agent
- QAgent - continuous state and action Q learner using iterative policy derivation
- RLLSTMAgent - simplified LSTM continuous action and state reinforcement learner
- SOM - simple Kohonen self-organizing map
- SOMQAgent - SOM-based state discretizing Q learner
- TabularQ - simplest possible discrete state and action Q learner
- RAAHN - real-time autoencoder augmented hebbian network