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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 court cases of the overseas 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 attractiveness, minority video games and agent coordination.
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Additional resources for Adaptive and Learning Agents: International Workshop, ALA 2011, Held at AAMAS 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers
Reinforcement Learning Transfer via Common Subspaces (a) Simple Pendulum 33 (b) Cartpole swing-up Fig. 3. 2 Inverted Pendulum to the Cartpole Swing-up For the second experiment, we transferred between the systems shown in Figure 3. A detailed description of the task’s dynamics can be found elsewhere . S1 is described by the θ1 and θ˙1 variables representing the angle and angular speed of the inverted pendulum respectively. S2 is described by θ2 , θ˙2 , x, and x˙ representing the angle, angular speed, position, and velocity of the cartpole, respectively.
Thus, the common task subspace Sc is described via the variables x and x˙ for mass #1 in both systems. Source Task: Single Mass System. The FVI algorithm was used to learn an optimal policy, π1∗ , for the first mass system. A parametric representation of the value function was used: V (s) = Ψ T Φ(s) and V (s) = (ψ1 ψ2 ψ3 ψ4 ψ5 )(x21,1 x1,1 x˙ 21,1 x˙ 1,1 1) . 5 We believe that carefully setting β1 and β2 may only be necessary when the source and target tasks are very dissimilar but we leave such explorations to future work.
Now choose the initial state s0 of ΓC from say ΓA ’s states. Accordingly, not all stage games in ΓC can be played. Since by deﬁnition of SSGs, each stage game must be played at least once, not all stochastic games can be translated into a sequential stage game. Together with the construction above, Prop. 1 follows. e. sequential stage games where each stage game is a cooperative one. e. all cooperative static games with a common agent set A which also share the same joint action set U . For better readability, we will write CCSG if we refer to CCSG(A, U ).