TY - GEN
T1 - A framework of an agent planning with reinforcement learning for e-pet
AU - Cheng, Sheng-Tzong
AU - Chang, Tun Yu
AU - Hsu, Chih Wei
PY - 2013/7/12
Y1 - 2013/7/12
N2 - E-pet is an animal-type robot companions, he can be physical or electronic. Reinforcement learning (RL) can be applied to the e-pet. However, the interactive instruction is constituted by complex activities. In this study, we proposed a framework that integrated AI planning technology into RL to generate the solution. In the framework, the e-pet interacts with human and includes two components: environment and agent. The agent exploits AI planning to seek goal state and Markov decision process (MDP) to choose the action and updates each Q-value using Q-learning algorithm. And we proposed the three-level subsumption architecture which including instinct level, perception level, and planning level. We build layers corresponding to each level of competence and can simply add a new layer to an existing set to move to the next higher level of overall competence. We implement the e-pet in a 3D model and train the agent. Experimental result shows that the update of Q-table reduces the number of planning states in the framework.
AB - E-pet is an animal-type robot companions, he can be physical or electronic. Reinforcement learning (RL) can be applied to the e-pet. However, the interactive instruction is constituted by complex activities. In this study, we proposed a framework that integrated AI planning technology into RL to generate the solution. In the framework, the e-pet interacts with human and includes two components: environment and agent. The agent exploits AI planning to seek goal state and Markov decision process (MDP) to choose the action and updates each Q-value using Q-learning algorithm. And we proposed the three-level subsumption architecture which including instinct level, perception level, and planning level. We build layers corresponding to each level of competence and can simply add a new layer to an existing set to move to the next higher level of overall competence. We implement the e-pet in a 3D model and train the agent. Experimental result shows that the update of Q-table reduces the number of planning states in the framework.
UR - http://www.scopus.com/inward/record.url?scp=84879847984&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879847984&partnerID=8YFLogxK
U2 - 10.1109/ICOT.2013.6521220
DO - 10.1109/ICOT.2013.6521220
M3 - Conference contribution
AN - SCOPUS:84879847984
SN - 9781467359368
T3 - ICOT 2013 - 1st International Conference on Orange Technologies
SP - 310
EP - 313
BT - ICOT 2013 - 1st International Conference on Orange Technologies
T2 - 1st International Conference on Orange Technologies, ICOT 2013
Y2 - 12 March 2013 through 16 March 2013
ER -