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wheel of the real Robertino robot
3-wheeled robot has learnt to balance two poles on top of each other


RNNs control fast weight nets for robot control
above: wheel of the real bot
3-wheeled reinforcement learning robot (with distance sensors) learns without a teacher to balance two poles with a joint indefinitely. The neurons of its recurrent neural networks (RNNs) co-evolve.
jointed pole about to crash
Above: 3 RNNs compute quickly changing weight values for 3 fast weight networks steering the 3 wheels of the robot living in a realistic 3D physics simulation. Left: still trying to learn to balance the two poles.
More about ESP in the page of Tino Gomez

More on Robot Learning

Paper: F. J. Gomez and J. Schmidhuber. Evolving modular fast-weight networks for control. In W. Duch et al. (Eds.): Proc. ICANN'05, LNCS 3697, pp. 383-389, Springer, 2005. PDF.


Cogbotlab .
cart with long pole and short pole
More work on coevolving recurrent neurons:

F. Gomez and J. Schmidhuber. Co-evolving recurrent neurons learn deep memory POMDPs. In Proc. of the 2005 conference on genetic and evolutionary computation (GECCO), Washington, D. C., pp. 1795-1802, ACM Press, New York, NY, USA, 2005. Nominated for Best Paper in Coevolution. PDF. Simultaneously evolves networks at two levels of granularity: full networks and neurons. Applied to POMDP learning tasks that require to create short-term memories of up to thousands of time steps, the method is faster and simpler than the previous best conventional reinforcement learning systems.

Related work on fast weights: J. Schmidhuber. Learning to control fast-weight memories: An alternative to recurrent nets. Neural Computation, 4(1):131-139, 1992. PDF. HTML. Compare pictures (German).
A slowly changing, gradient-based feedforward neural net learns to quickly manipulate short-term memory in fast synapses of another net.

More fast weights: J.  Schmidhuber. Reducing the ratio between learning complexity and number of time-varying variables in fully recurrent nets. In Proc. ICANN'93, Amsterdam, pages 460-463. Springer, 1993. PDF. HTML.
In a certain sense, short-term memory in fast synapses can be more efficient than short-term memory in recurrent connections.

A related co-evolution method called COSYNE:

F. Gomez, J. Schmidhuber, R. Miikkulainen. Accelerated Neural Evolution through Cooperatively Coevolved Synapses. Journal of Machine Learning Research (JMLR), 9:937-965, 2008. PDF.

F. Gomez, J. Schmidhuber, and R. Miikkulainen (2006). Efficient Non-Linear Control through Neuroevolution. Proceedings of the European Conference on Machine Learning (ECML-06, Berlin). PDF.
A new, general method that outperforms many others on difficult control tasks.

More recent work of 2013: Compressed Network Search Finds Complex Neural Controllers with a Million Weights, learns to drive without a teacher from raw high-dimensional video input

Related work on evolution for supervised sequence learning: a new class of learning algorithms for supervised RNNs, which outperforms previous methods: Evolino (2005).

Related work on Compressed Network Evolution(1995-): Many practical algorithms can evolve hundreds of adaptive parameters, but not millions. Ours can, by evolving compact, compressed descriptions (programs) of huge networks.

J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez. Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Amsterdam, 2013. PDF.

J. Koutnik, F. Gomez, J. Schmidhuber. Searching for Minimal Neural Networks in Fourier Space. The 3rd Conference on Artificial General Intelligence (AGI-10), 2010. PDF.

J.  Schmidhuber. Discovering solutions with low Kolmogorov complexity and high generalization capability. In A. Prieditis and S. Russell, editors, Machine Learning: Proceedings of the Twelfth International Conference (ICML 1995), pages 488-496. Morgan Kaufmann Publishers, San Francisco, CA, 1995. PDF . HTML.

Fibonacci web design
by J. Schmidhuber