Autonomous Agents by Vedran Kordic

By Vedran Kordic

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1994). On-line Q-learning using connectionist systems, Technical Report CUED/F-INFENG/TR166, Cambridge University. Russell, S. J. & Zimdars, A. (2003). Q-decomposition for reinforcement learning agents, International Conference on Machine Learning, AAAI Press, pp. 656–663. Smart, W. D. & Kaelbling, L. P. (2002). Effective reinforcement learning for mobile robots, International Conference on Robotics and Automation, Vol. 4, IEEE, pp. 3404–3410. Sprague, N. & Ballard, D. H. (2003). Multiple-goal reinforcement learning with modular Sarsa(0), International Joint Conference on Artificial Intelligence, pp.

Notice that these three tasks are randomly generated with the condition that each state could reach the terminal state. The accelerated effects of the transfer method are shown in Fig. 20. The results show that the transfer method is not only used in one of transfer types, but also in the synthetic case. That is the reason why we discuss the transfer method rather than the simple transfer method. R R R random 9001 8001 7001 6001 5001 4001 3001 2001 1001 1 transferred 1 201 401 601 801 1001 1201 1401 1601 1801 number of episodes number of steps number of steps (a) (b) (c) Fig.

2(a). In the up-scaling case, we choose the 6x6 grid world as a source task and 8x8, 10x10, 12x12, 14x14, 16x16, 18x18, and 20x20 grid world as target tasks. In down-scaling case, we choose the 20x20 grid world as a source task and 6x6, 8x8, 10x10, 12x12, 14x14, 16x16, and 18x18 grid world as target tasks. The results are shown in Fig. 11, where the simple transferred policy is derived from the simple transfer method and the transferred policy is derived from the transfer method. We could discover that regardless of the size is changed in a target task, the simple transferred policy still performs very close to the optimal policy and the transferred policy doe not always perform well.

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