If sorrounding countries strengthened the provocations and sanctions, , , , , do you think that prewar Japan around 80 years ago did not plunge into the war?
If a nuclear war happens and kills a huge amount of people and also radioactively contaminates whole the earth, or if robots with sophisticated artificial intelligence kill a huge mount of people and push all the people on the earth into trembling with the fear of them everyday, do you think that historians in the future will give the judgment to the modern development of science and technology that what is bad is only the way to utilize it? Anyway, at first, I pray historians may still exist in the future.
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Japanese version is here.
Sorry still under construction. Last update was done on Oct. 20, 2016
Thanks to the wonderful results by Hassabis et al. in
this field (RL+NN) is now suddenly collecting attentions.
End-to-end reinforcement learning using a neural network (from sensors to motors) is our original idea
|Real Robot Applications with Raw Pixel Image Inputs||Click HERE|
|Emergence of Communications
(Signal Discretization, Grounded Communication, Dynamic Communication)
|Higher Function Emergence Using a Recurrent Neural Network (RNN)
(Memory, Prediction, Exploration, Attention, Other Contextual Bahaviors)
|Function Emergence Using a Layered Neural Network
(Color Constancy, Hand Reaching (Hand-Eye Coordination), Tool Use, Sensor Motion)
|Concept & Overview||Click HERE|
|RL with Chaotic NN Aiming to the Emergence of THINKING||Click HERE|
|Other Works||Click HERE|
|Early works in RL+NN||Click HERE|
<Real Robot Application>
Kissing AIBO task (2008)
The world's first Deep Q Network applied to a Real Robot!
6,240 raw RGB image signals are put into a 5 layer Q-network
From only the reward for kiss and penalty for disapperance of the target AIBO, the AIBO learns to recognize the target AIBO and go toward it. ICONIP2008 paper
The following paper is for the previous task, but the analysis of hidden representation is interesting.
The world's first end-to-end reinforcement learning in a real robot!
Not deep but 3 layer neural network with 1540 raw gray-scale pixel inputs
Actor-Critic in continuous sensor and motor spapce.
<Emergence of Communication>
Grounded Communication Task (2011)
The world's first Grounded Communication through reinforcement learning in a real robot!
Communication protocol is not given in advance
Completely Independent Learning (No information is communicated between the agents)
Reward is given to both agents when the robot reaches the red area.
Transmitter(5layer): Image inputs (so we can say "Grounded"), Sound outputs
Receiver(3layer): Sound inputs, Motion command outputs
Appropriate sound communication to move the robot to the goal
is acquired through reinforcement learning.
Discretization of Communication Signal (2005)
Continuous communication signal is discretised autonomously through reinforcement learning in a noisy environment without any directions!
Transmitter (cannot move): Input: Location of the receiver, Output: Continuous signal
Receiver (cannot see): Input: The signal from the transmitter, Ouput: Move (in one-dimensional space)
Communication: Two continuous signals are sent one by one (t=1, t=2) through one channel
Receiver has to understand the distance from the two successive signals using its RNN.
Reward: The distance between the agents is smaller than 0.11.
The location of the receiver is represented two bit binary signal as above in noisy environment.
ICANNGA 2005 paper
Dynamic Communication (Negotiation)(1999)
Learning of 3 communication chances to avoid conflict.
4 agents are picked up randomly among 8 agents at each episode.
3 communication chances are given, and -1 or 1 is chosen as a signal according to the com output.
At each chance, the agents can receive the 4 signals including their own signal that are input of the RNN.
After 3 communication chances, each agent has to choose which path to go circle(1) or triangle(-1).
No conflict -> reward, Conflict -> penalty
Sample negotiation after learning are shown above.
Individuality emerge among the agenet as above.
ISADS 1999 paper
<Concept and Overview of Our Research>
(Don't try to understand the Brain Functions!?)
Emergence of Intelligence through Reinforcement Learning with a Neural Network,
Advances in Reinforcement Learning, Abdelhamid Mellouk (Ed.), InTech, pp.99-120, 2011. 2.
<Emergence of Static Functions>
Emergence of Color constancy (2012)
Katsunari Shibata and Shunsuke Kurizaki:
Emergence of Color Constancy Illusion through Reinforcement Learning with a Neural Network,
Proc. of ICDL-EpiRob (Int'l Conf. on Development and Learning - Epigenetic Robotics) 2012, PID2562951.pdf, 2012. 11
pdf File (6 pages, 1.6MB)
(Emergence of Hand-Eye Coordination & Reaching Arm Movement)
Internal Representation for Tool use (2003)
Katsunari Shibata & Koji Ito:
Hidden Representation after Reinforcement Learning of Hand Reaching Movement with Variable Link Length,
Proc. of IJCNN (Int'l Joint Conf. on Neural Networks) 2003, 1475-674.pdf, pp.2619-2624, 2003. 7
pdf File (6 pages, 764kB)
Hand-Eye Coordination and Reaching Movement (2002)
Katsunari Shibata & Koji Ito:
Effect of Force Load in Hand Reaching Movement Acquired by Reinforcement Learning,
Proc. of Int'l Conf. on Neural Information Processing Systems (ICONIP '02), Vol. 3, pp. 1444-1448, 2002.11
pdf File (5 pages, 200kB)
<Higher Functions using a Recurrent Neural Network>
Very interesting behavior (2009)
Hioroki Utsunomiya and Katsunari Shibata:
Contextual Behavior and Internal Representations Acquired by Reinforcement Learning with a Recurrent Neural Network in a Continuous State and Action Space Task,
Advances in Neuro-Information Processing, Lecture Notes in Computer Science, Proc. of ICONIP (Int'l Conf. on Neural Information Processing) 08, Vol. 5507, pp. 970--978, 5507-0970.pdf (CD-ROM), 2009
pdf File (9pages, 472kB)
Prediction and Reaction to unexpected situation (2013)
Learning of catching an moving object that sometimes becomes invisible after a while from start. The initial location, speed and angle of the object are set randomly at each episode. Both the timing and location for catch have to be learned. Flexible response for sudden motion change is also required.
Katsunari Shibata and Kenta Goto:
Emergence of Flexible Prediction-Based Discrete Decision Making and Continuous Motion Generation through Actor-Q-Learning,
Proc. of Int'l Conf. on Development and Learning and on Epigenetic Robotics (ICDL-Epirob) 2013, ID 15 (CDROM) 2013. 8
pdf file (6 pages, 684KB)
Discretization of Communication Signal by Noise Addition, (2005)
Discretization of Series of Communication Signals in Noisy Environment by Reinforcement Learning,
Ribeiro et al(eds.), Adaptive and Natural Computing Algorithms, Proc. of the 7th Int'l Conf. in Adaptive and Natural Computing Algorithms (ICANNGA'05), pp. 486-489, 2005. 3
pdf File (4 pages, 224kB)
Emergence of Sensor motion, Active Perception and Recognition, Actor-Q-learning (2014)
A.A.M. Faudzi and Katsunari Shibata:
Acquisition of Context-Based Active Word Recognition by Q-Learning Using a Recurrent Neural Network,
J.-H. Kim et al. (eds.), Robot Intelligent Technoloy and Application, Vol. 2, Advances in Intelligent System and Computing, 274, pp. 191-200, 2014.4
pdf file (10 pages, 1.7MB)
Katsunari Shibata, Tetsuo Nishino & Yoichi Okabe:
Active Perception and Recognition Learning System Based on Actor-Q Architecture,
J.-H. Kim et al. (eds.), Robot Intelligent Technoloy and Application, Vol. 2, Advances in Systems and Computers in Japan, Vol. 33, No. 14, pp. 12-22, 2002. 12
pdf file (11 pages, 5.6MB)
Emergence of Exploration
Learning of exploration for an unseen goal.
Kenta Goto and Katsunari Shibata:
Acquisition of Deterministic Exploration and Purposive Memory through Reinforcement Learning with a Recurrent Neural Network,
Proc. of SICE Annual Conf. 2010, FB03-1.pdf, 2010. 8
pdf file (7 pages, 312KB)
The effect that learning in one room gives to the learning in 4 rooms environment was observed.
Deterministic exploration behavior acquired through reinforcement learning can be considered as temporal abstract actions.
Learning of Deterministic Exploration and Temporal Abstraction in Reinforcement Learning,
Proc. of SICE-ICCAS (SICE-ICASE Int'l Joint Conf.), pp. 4569-4574, 2006. 10
pdf file (6 pages, 462B)
Spatial Abstraction and Knowledge Transfer in Reinforcement Learning Using a Multi-Layer Neural Network,
Proc. of ICDL5 (Fifth Int'l Conf. on Development and Learning) 2006, 36 (CD-ROM), 2006. 5BR> pdf file (6 pages, 712B)
Emergence of Attention and Associative Memory (2001)
Formation of Attention and Associative Memory Based on Reinforcement Learning,
Proc. of ICCAS (Int'l Conf. on Control, Automation and Systems) 2001, pp. 9-12, 2001. 10
pdf File (4 pages, 136kB)
<Learning of Temporal Events (Using Chaos or Causality)>
Completely Novel Reinforcement Learning Using a Chaotic Neural Network, expecting to be the Breakthrough for emergence of "Thinking" (2015)
Katsunari Shibata and Yuta Sakashita:
Reinforcement Learning with Internal-Dynamics-based Exploration Using a Chaotic Neural Network,
Proc. of Int'l Joint Conf. on Neural Networks (IJCNN)2015, 2015.7
pdf file (8 pages, 843KB)
More Effective memory than Eligibility Trace, Subjective Time Scale (2014)
Causality Traces for Retrospective Learning in Neural Networks - Introduction of Parallel and Subjective Time Scales -,
Proc. of Int'l Joint Conf. on Neural Networks, P542 (CD-ROM) , 2014.7
pdf file (8 pages, 302KB)
Growing Neural Network, Axon grows according to Diffused Error signals , 2004)
Ryusuke Kurino, Masanori Sugisaka & Katsunari Shibata:
Growing Neural Network with Hidden Neurons,
Proc. of The 9th AROB (Int'l Sympo. on Artificial Life and Robotics), Vol. 1, pp. 144-147, 2004. 1
pdf File (4 pages, 191kB)
Those works have been done by the cooperation of my colleagues. There are many ones whose name is not in the above, but the above works were done by the basis that all the members have worked.