Exploring in environments with high-dimensional observations is hard. One
promising approach for exploration is to use intrinsic rewards, which often
boils down to estimating "novelty" of states, transitions, or trajectories with
deep networks. Prior works have shown that conditional prediction objectives
such as masked autoencoding can be seen as stochastic estimation of
pseudo-likelihood. We show how this perspective naturally leads to a unified
view on existing intrinsic reward approaches: they are special cases of
conditional prediction, where the estimation of novelty can be seen as
pseudo-likelihood estimation with different mask distributions. From this view,
we propose a general framework for deriving intrinsic rewards -- Masked Input
Modeling for Exploration (MIMEx) -- where the mask distribution can be flexibly
tuned to control the difficulty of the underlying conditional prediction task.
We demonstrate that MIMEx can achieve superior results when compared against
competitive baselines on a suite of challenging sparse-reward visuomotor tasks.

提出了一种用于探索的通用框架 Masked Input Modeling for Exploration (MIMEx)，它能够通过灵活调整掩码分布来控制条件预测任务的难度，并在一系列挑战性的稀疏奖励视觉运动任务中取得了优异的结果。

MIMEx：基于掩码输入建模的内在奖励

MIMEx: Intrinsic Rewards from Masked Input Modeling

It is desirable to predict the behavior of traffic participants conditioned
on different planned trajectories of the autonomous vehicle. This allows the
downstream planner to estimate the impact of its decisions. Recent approaches
for conditional behavior prediction rely on a regression decoder, meaning that
coordinates or polynomial coefficients are regressed. In this work we revisit
set-based trajectory prediction, where the probability of each trajectory in a
predefined trajectory set is determined by a classification model, and
first-time employ it to the task of conditional behavior prediction. We propose
RESET, which combines a new metric-driven algorithm for trajectory set
generation with a graph-based encoder. For unconditional prediction, RESET
achieves comparable performance to a regression-based approach. Due to the
nature of set-based approaches, it has the advantageous property of being able
to predict a flexible number of trajectories without influencing runtime or
complexity. For conditional prediction, RESET achieves reasonable results with
late fusion of the planned trajectory, which was not observed for
regression-based approaches before. This means that RESET is computationally
lightweight to combine with a planner that proposes multiple future plans of
the autonomous vehicle, as large parts of the forward pass can be reused.

本文提出了一种基于集合的轨迹预测方法，将其应用于无人驾驶车辆的条件行为预测，并通过图形编码器和计划轨迹的后期融合，实现了与回归方法相当的性能。

RESET: 重访轨迹集以进行条件行为预测

RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction

Predicting future motions of road participants is an important task for
driving autonomously in urban scenes. Existing models excel at predicting
marginal trajectories for single agents, yet it remains an open question to
jointly predict scene compliant trajectories over multiple agents. The
challenge is due to exponentially increasing prediction space as a function of
the number of agents. In this work, we exploit the underlying relations between
interacting agents and decouple the joint prediction problem into marginal
prediction problems. Our proposed approach M2I first classifies interacting
agents as pairs of influencers and reactors, and then leverages a marginal
prediction model and a conditional prediction model to predict trajectories for
the influencers and reactors, respectively. The predictions from interacting
agents are combined and selected according to their joint likelihoods.
Experiments show that our simple but effective approach achieves
state-of-the-art performance on the Waymo Open Motion Dataset interactive
prediction benchmark.

利用交互作用代理之间的关系，将联合预测问题分解为边际预测问题，继而通过使用边际预测模型和条件预测模型，预测行人的轨迹，并根据它们的联合可能性进行组合和选择。