Due to its iconic nature ( i.e ., perceptual resemblance to or natural association with the referent), drawings serve as a powerful tool to communicate concepts transcending language barriers (Fay et al., 2014). In fact, we humans started to use drawings to convey messages dating back to 40,000-60,000 years ago (Hoffmann et al., 2018; Hawkins et al., 2019).

However, unlike QMIX, there are no inherent constraints on factoring the critic. We thus also employ a nonmonotonic factorisation and empirically demonstrate that its increased representational capacity allows it to solve some tasks that cannot be solved with monolithic, or monotonically factored critics.

Multi-agent control is a central theme in the Cyber-Physical Systems (CPS) . However, current control methods either receive non-Markovian states due to insufficient sensing and decentralized design, or suffer from poor convergence.

The pseudocode for DRE-MARL training is shown in Algorithm 20, which takes the following steps. The property of the received reward in this environment is set to be collaborative. It is a scenario with two agents and three landmarks. Navigation and Reference is that the target landmark of each agent is only known to its partner. We use the abbreviation REF to denote this environment.

This section is meant to give an overview of our opensource code. Together with this git repo, we include a'tutorial colab' - a Jupyter notebooks that can be run in the browser without requiring any local installation at We view this open-source effort as a major contribution of our paper. We present the testbed pseudocode in this section. Recall from Section 3.1 that we We now describe the other parameters we use in the Testbed. In this section, we describe the benchmark agents in Section 3.3 and the choice of various Step 3: compute likelihoods for n = 1, 2, . . .

Algorithm 1 shows the execution rules of parallel programs. Terminate the program if no subsequent subroutine exists. Compute the cost of each possible allocation based on the auxiliary functions. The common hyperparameters are listed below. Name V alue learning rate 3e-4 training steps 10M update batch size 256 number of rollout threads 8 rollout buffer size 4096 8 weight of value loss 0.1 weight of policy loss 1 weight of entropy loss 0.01 In cooperative settings, the goal input of the assistive agent is the leading agent's goal.

Extracting the interaction rules of biological agents from movement sequences pose challenges in various domains.