By Edward Robinson, Peter McBurney, Xin Yao (auth.), Peter Vrancx, Matthew Knudson, Marek Grześ (eds.)

This quantity constitutes the completely refereed post-conference lawsuits of the foreign Workshop on Adaptive and studying brokers, ALA 2011, held on the tenth foreign convention on independent brokers and Multiagent platforms, AAMAS 2011, in Taipei, Taiwan, in might 2011. The 7 revised complete papers offered including 1 invited speak have been conscientiously reviewed and chosen from a number of submissions. The papers are geared up in topical sections on unmarried and multi-agent reinforcement studying, supervised multiagent studying, version and studying in dynamic environments, studying belief and recognition, minority video games and agent coordination.

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Additional info for Adaptive and Learning Agents: International Workshop, ALA 2011, Held at AAMAS 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers

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A detailed descriptions of the tasks’ equations of motion can be found elsewhere [4]. S1 is described by the {x1,1 , x˙ 1,1 } variables, representing the position and the velocity of the mass M1,1 . S2 = {x1,2 , x˙ 1,2 , x2,2 , x˙ 2,2 }, representing the position and the velocity of M1,2 and M2,2 . A reward of +1 is given to the agent of system one if the position of the mass M1,1 is 1 and −1 otherwise. On the other hand, a reward of +10 is given to the agent of system two if the position and the velocity of the mass M1,2 is 1 and 0 respectively, and otherwise a reward of −10 is given.

1. 4 Transfer Learning in RL Tasks In transfer learning, there typically exists a source and a target task, where the goal is to increase the performance and to reduce the learning times in the target task agent [10]. This is done by allowing an agent in a target task to reuse knowledge and behaviors 1 In case of stochastic MDPs then q(a) on line 7 is found by averaging over a number of successor states. Reinforcement Learning Transfer via Common Subspaces 25 Algorithm 1. Fitted Value Iteration for deterministic MDPs 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: Randomly sample m states from the MDP Ψ ←0 n ← the number of available actions in A repeat for i = 1 → m do for all a ∈ A do q(a) ← R(s(i) ) + γV (s(j) ) y (i) ← max q(a) 2 (i) Ψ ← arg minΨ m − Ψ T Φ(s(i) )) i=1 (y until Ψ Converges acquired by an agent in one or more source tasks.

We refer to this class as common static games. If a static game is played repeatedly by the same agents then the game is called a repeated game. The term stage game refers to the (static) game that is played in a fixed state s ∈ S of a stochastic game. Since states of stochastic games usually are visited repeatedly, a stage game is also a repeated game. As shown in Sect. e. they learn a policy by learning strategies for each stage game that arises in the different states of a stochastic game. The focus of this work is a new interesting subclass of stochastic games that we named sequential stage games.

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