Independent Q-learning for BPD and Congestion Game #51
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…f results with original paper
ffelten
approved these changes
Mar 21, 2024
momaland/learning/iql/iql.py
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| cap_min, cap_max, mix_min, mix_max = self.g_cap_min, self.g_cap_max, self.g_mix_min, self.g_mix_max | ||
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| # Normalize the rewards | ||
| cap_norm = (reward[0] - cap_min) / (cap_max - cap_min) |
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I believe we have a NormalizeReward wrapper made for this. Is it different?
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The NormalizeReward wrapper normalizes the rewards that the agents receive directly. In the BDP environment, agents can be rewarded according to two reward schemes (local/global), but the reported results in the graphs are always using the (normalized) 'global' reward scheme regardless of what the agents receive. This method is primarily used to compute these normalized global rewards.
rradules
approved these changes
Mar 21, 2024
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