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

This quantity constitutes the completely refereed post-conference complaints of the foreign Workshop on Adaptive and studying brokers, ALA 2011, held on the tenth foreign convention on self reliant brokers and Multiagent structures, AAMAS 2011, in Taipei, Taiwan, in could 2011. The 7 revised complete papers awarded including 1 invited speak have been rigorously reviewed and chosen from various submissions. The papers are prepared 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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E. SSG ⊂ SG. In order to prove this proposition first consider the following construction. It allows us to reformulate any sequential stage game as stochastic game. Let Γ = A, U , G, G0 , n0 , g be an arbitrary sequential stage game. Then a corresponding stochastic game Γ = s0 , S, A , U , f, {ρi }i∈A is constructed by: – A = A and U = U . m – recall the definition of the set of games G. e. S = s∅ , s0 , . . , s0 , s1 , . . , s1 , . . , sm , . . , sm , s∞ . Here, svj denotes the state that is obtained when game Gj was played for the v-th iteration.

Static (stateless) game) A static (stateless) game is defined by a tuple Γ = A, U , {ρi }i∈A , where A is a set of agents playing the game. The set of joint actions is given as U = ×i∈A Ai , where Ai denotes the actions available to agent i ∈ A. The set of all reward functions is denoted as {ρi }i∈A having ρi : U → R for all agents i ∈ A. In a static game, we will use the term strategy instead of policy to reflect the loss of the state signal. A strategy for an agent i hence is given by σi : Ai → [0, 1].

Finally, Sect. 6 closes the paper with a discussion and an outlook on future work. 2 Background and Related Work Reinforcement learning problems for single agent systems are mostly framed as Markov Decision Processes (cf. Sect. 3). Basically, a reinforcement learning agent senses the current state st ∈ S of its environment and selects and executes an action at ∈ A. It then perceives the resulting state of the environment and a scalar reward R(st , at ), that reflects the influence of the action on the environment.

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