We consider the 3 fully cooperative tasks from the original set shown in Figure 1(a):Spread, Comm,andReference. "Use feature normalization" refers to whether the feature normalization is applied to the networkinput. In this appendix section, we include results which demonstrate the benefit of parameter sharing. Note that our global state to the value network has agent-specific information, such as available actions and relative distances to other agents. When an agent dies, these agent-specific features become zero, while the remaining agent-agnostic features remain nonzero -this leads to adrastic distribution shift in the critic input compared to states in which the agent is alive.

Inthiswork, we carefully study the performance of PPO in cooperative multi-agent settings.

The scarcity of labeled dataset leads us to consider the integration of data augmentation and active learning.

Whileprevious work tackles this issue by using explicit labeling on the spuriously correlated attributes or presuming a particular bias type, we instead utilize a cheaper, yet generic form of human knowledge, which can be widely applicable to various types ofbias.

The visited node contains a panoramic image and the corresponding feature is derived through a mean pooling operation. The action node contains the rawfeature from the corresponding image andtheangleinformation. Theplanner concatenates thelanguage encodingc and agent statesht with the node features to acquire the contextual information. The final node information {vt} is planned through several steps of message passing operations.

Still, many relevant tasks such as object manipulation pose significant challenges forRL.

Such a weak generalization capability hinders the applicability of current deepfake detectors.

We argue that there are two major bottlenecks.