Research Article
Experience Weighted Learning in Multiagent Systems
Algorithm 2
Experience weighted learning.
| (1) | repeat | | (2) | repeat | | (3) | i = 1, t = 1 | | (4) | repeat | | (5) | Choose an opponent randomly | | (6) | | | (7) | Choose an action according to strategy | | (8) | | | (9) | t = t + 1 | | (10) | until t equals the maximum period number | | (11) | Update the probability of actions according to | | (12) | | | (13) i = i + 1 | | (14) | until i = the total number of all the agents | | (15) | until predefined iteration number |
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