A promising approach to incorporating player information into sports statistics is deep agent representation.

Exploration is a lifelong process. In this paper, we propose a paradigm change in the formulation and evaluation of task-agnostic exploration.

However, distributed algorithms for learning relatedness among tasks are not resilient in the presence of Byzantine agents. In this paper, we present an approach for Byzantine resilient distributed multi-task learning. We propose an efficient online weight assignment rule by measuring the accumulated loss using an agent's data and its neighbors' models. A small accumulated loss indicates a large similarity between the two tasks.

We thank all the reviewers for their valuable comments and appreciation of the ideas and results presented in the paper. We summarize the main questions from the reviewers and address them separately below. T o Reviewer #1 Q1: Network connectivity is presumably known . . . it seems all the graphs considered are com-3 We note that the network connectivity is not assumed to be known. T o Reviewer #3 Q1: Scope of the paper/Missing related work. " and "FedNAS" are about We can add an explanation to clarify the MTL scope of the paper.

However, existing synthetic data generation tools that provide referring expressions generally neglect nonverbal gestures.

We consider Diplomacy, a 7-player board game designed to accentuate dilemmas resulting from many-agent interactions.

To avoid unnecessary confusion, we notate the joint distribution of the L.H.S as We show that the infimum of the R.H.S. is reached when Then we can update the joint distribution for the L.H.S. with The start steps for employing SPD to obtain pseudo-reward 5000 α The factor of the regularized term in Eq. (6) 0 B We prove the triangle inequality by contradictions similar to iii). Each agent has to resolve to select the action from its discrete action space to move around. Neural Network (RNN) is used in the policy to alleviate the partial observability. WW W of edge { i, j} depicts agents' relative relations. Synergy Pattern Function ζ A general function which could depict agents' relative relations.

As for the single agent, unsupervised learning has been incorporated into RL to acquire diverse skills for the agent without extrinsic reward from the environment, and this scenario is known as unsupervised reinforcement learning (URL).

ML, preferences and rankings are commonly learned by fitting a probabilistic model to noisy preference data. The behavior of this learning process from the view of economic theory has previously been studied for the case where the data consists of rankings. In practice, it is more common to have only pairwise comparison data, and the formal properties of the associated learning problem are more challenging to analyze. We show that a large class of random utility models (including the Thurstone-Mosteller Model), when estimated using the MLE, satisfy a Pareto efficiency condition. These models also satisfy a strong monotonicity property, which implies that the learning process is responsive to input data. On the other hand, we show that these models fail certain other consistency conditions from social choice theory, and in particular do not always follow the majority opinion. Our results inform existing and future applications of random utility models for societal decision making.