Sachin Konan, Esmaeil Seraj, Matthew Gombolay
We lifted 10 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| deepmind/pysc2 | canonical | 6 of 7 |
| core-robotics-lab/infopg | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| MOA_PolicyHelper | Ran | core-robotics-lab/infopg/pistonball/policy_piston.py pointer only (licence: GPL-3.0) · get_code("593ef84a0fec9ab2") |
| memoize | Ran | deepmind/pysc2/pysc2/lib/memoize.py pointer only (licence: Apache-2.0) · get_code("1314ce1206a217e8") |
| numpy_to_python | Ran | deepmind/pysc2/pysc2/lib/actions.py pointer only (licence: Apache-2.0) · get_code("795b48aa13444f75") |
| piece_wise_linear | Ran | deepmind/pysc2/pysc2/lib/colors.py pointer only (licence: Apache-2.0) · get_code("1902983401727afd") |
| shuffled_hue | Ran | deepmind/pysc2/pysc2/lib/colors.py pointer only (licence: Apache-2.0) · get_code("094587ca8ce60a1a") |
| smooth_hue_palette | Ran | deepmind/pysc2/pysc2/lib/colors.py pointer only (licence: Apache-2.0) · get_code("ee4a38c9d2b4ba59") |
| summarize_array_diffs | Ran | deepmind/pysc2/pysc2/lib/np_util.py pointer only (licence: Apache-2.0) · get_code("b9be4b0c3dee19d6") |
| spatial | Not yet run | deepmind/pysc2/pysc2/lib/actions.py pointer only (licence: Apache-2.0) · get_code("93e90c9405886816") |
| train_adv_infopg_5 | Not yet run | core-robotics-lab/infopg/pistonball/test_piston_ball.py pointer only (licence: GPL-3.0) · get_code("d04972da01192ed6") |
| train_infopg_5 | Not yet run | core-robotics-lab/infopg/pistonball/test_piston_ball.py pointer only (licence: GPL-3.0) · get_code("8673d75351fb3782") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Information sharing is key in building team cognition and enables coordination and cooperation. High-performing human teams also benefit from acting strategically with hierarchical levels of iterated communication and rationalizability, meaning a human agent can reason about the actions of their teammates in their decisionmaking. Yet, the majority of prior work in Multi-Agent Reinforcement Learning (MARL) does not support iterated rationalizability and only encourage inter-agent communication, resulting in a suboptimal equilibrium cooperation strategy. In this work, we show that reformulating an agent's policy to be conditional on the policies of its neighboring teammates inherently maximizes Mutual Information (MI) lower-bound when optimizing under Policy Gradient (PG). Building on the idea of decision-making under bounded rationality and cognitive hierarchy theory, we show that our modified PG approach not only maximizes local agent rewards but also implicitly reasons about MI between agents without the need for any explicit ad-hoc regularization terms. Our approach, InfoPG, outperforms baselines in learning emergent collaborative behaviors and sets the state-of-the-art in decentralized cooperative MARL tasks. Our experiments validate the utility of InfoPG by achieving higher sample efficiency and significantly larger cumulative reward in several complex cooperative multi-agent domains.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.08484")
get_code_for_paper("2201.08484")
have("2201.08484")
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