Recent slot-centric generative models attempt to decompose scenes into entities in a self-supervised manner by reconstructing pixels. In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases. Recent test-time adaptation methods use auxiliary self-supervised losses to adapt the network parameters to each test example independently and have shown promising results towards generalization outside the training distribution for the task of image classification. Ĭurrent visual detectors, though impressive within their training distribution, often fail to parse out-of-distribution scenes into their constituent entities. The experimental results in the Overcooked game environment demonstrate that our method outperforms current state-of-the-art methods when coordinating with different-level partners. Furthermore, an analysis of the learning process of the algorithm shows that it can efficiently overcome cooperative incompatibility. We further specify the framework and propose a practical algorithm that leverages knowledge from game theory and graph theory. To address this issue, we propose the Cooperative Open-ended LEarning (COLE) framework, which constructs open-ended objectives in cooperative games with two players from the perspective of graph theory to assess and identify the cooperative ability of each strategy. However, these approaches can result in a loss of learning and an inability to cooperate with certain strategies within the population, known as cooperative incompatibility. Previous algorithms have attempted to address this challenge by optimizing fixed objectives within a population to improve strategy or behaviour diversity. Zero-shot coordination in cooperative artificial intelligence (AI) remains a significant challenge, which means effectively coordinating with a wide range of unseen partners.
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