We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| proroklab/adversarial_comms | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| dir_path | Ran | proroklab/adversarial_comms/adversarial_comms/train_policy.py pointer only (licence: GPL-3.0) · get_code("a629a73b499b2b90") |
| path_to_hash | Ran | proroklab/adversarial_comms/adversarial_comms/evaluate.py pointer only (licence: GPL-3.0) · get_code("e207eb2521be40e6") |
| trial_dirname_creator | Ran | proroklab/adversarial_comms/adversarial_comms/train_policy.py pointer only (licence: GPL-3.0) · get_code("2eb3264d27779217") |
| update_dict | Ran | proroklab/adversarial_comms/adversarial_comms/train_policy.py pointer only (licence: GPL-3.0) · get_code("922ad6210bf86e67") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. These works use models of cooperative multi-agent systems whereby agents strive to achieve a shared global goal. When considering agents with self-interested local objectives, the standard design choice is to model these as separate learning systems (albeit sharing the same environment). Such a design choice, however, precludes the existence of a single, differentiable communication channel, and consequently prohibits the learning of inter-agent communication strategies. In this work, we address this gap by presenting a learning model that accommodates individual non-shared rewards and a differentiable communication channel that is common among all agents. We focus on the case where agents have self-interested objectives, and develop a learning algorithm that elicits the emergence of adversarial communications. We perform experiments on multi-agent coverage and path planning problems, and employ a post-hoc interpretability technique to visualize the messages that agents communicate to each other. We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.02616")
get_code_for_paper("2008.02616")
have("2008.02616")
Connect an agent — have() is free.