In our experiments, the fine-grained map, global semantic map, and multi-granularity map are of different sizes (asshowninFigure A)forsaving GPU memory. Object categories predicted by hallucination module. We use an Adam optimizer with a learning rate of 2.5e-4. Specifically,we consider the 10% area with 2 the highest probability in 2D distributionP and ˆP (as described in Section 3.3) as ground-truth andpredicted locations. From Table 1,this variant performs worse than our agent.

Withaback-offstrategyin the SGD step and an elitist strategy in the evolution step, it guarantees that the best fitness in the population will never degrade.

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I think the author needs to argue why Avalon is a better agent for real-world hidden role scenarios than other5 games? Among hidden role games, Avalon is one of the most popular and widely played games (according to6 boardgamegeek.com) Inthe real world, there are7 subtle cues which can often be misinterpreted when others are acting under uncertainty. AlphaGo-likemethods can be used when12 the board state alone is sufficient to determine the best move, but in imperfect information games it is necessary to13 consider how players acted to reach the current board state. We've added the following sentence: "Compared to14 MCTS-based methods like AlphaGo, CFR-based methods like DeepStack and DeepRole can soundly reason over15 hiddeninformation."16

Integrated development environments (IDEs) assist developers in understanding repository context using static analysis.

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We present a framework to train a structured prediction model by performing smoothing ontheinference algorithm itbuilds upon.