In what follows, we comment on certain key aspects of our proposed algorithms.

Setting up a well-designed reward function has been challenging for many reinforcement learning applications.

Here we prove the variational bound on the informativeness loss term (second term in Eq. (4)) that Recall that the speaker's belief states, Therefore, any other decoder would lend an upper bound on the informativeness loss term. In this case, the speaker's belief states are given by While Eq. (A.1) follows from [ Eq. (A.2) is equivalent to assuming that the listener's The main paper is available at https://openreview.net/pdf?id=O5arhQvBdH. Therefore, we treat it here as a discrete set. We therefore aim to bias our agents toward these systems. One way of achieving that is by regularizing the entropy of the speaker's communication vectors.

Policy optimization, i.e. algorithms that learn to make sequential decisions by local search on the agent's policy directly, is a widely used class of algorithms in reinforcement learning [

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.