For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? While MARL algorithms may be implemented for potentially harmful applications, we do not believe this work uniquely enables such applications. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] In the supplemental material (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? Our allocation proposal network and Q network are illustrated in Figures 7 and 8. Low-level action utility functions and mixing networks are similar to those described in Iqbal et al. [10] with the only 13 difference being a replacement of the RNN layers with standard fully connected layers.

In the current paper we relax the information structure of these classical frameworks by allowing arbitrary information arrivalprocesses.

Somewhat surprisingly, learning in a Markov Decision Process is most often considered under the performance criteria ofconsistency or regret minimization(see e.g.

On the analysis front, we establish lower bounds on the performance that is achievable by any non-anticipating policy in the presence of unknown information flows. We further show that our lower bounds can be achieved through suitable policy design.

Somewhat surprisingly, learning in a Markov Decision Process is most often considered under the performance criteria of consistency or regret minimization (see e.g.

Unfortunately, when we move away from the safe haven of two-agent strictly competitive games, a lot of these regularities disappear.