Autonomous agents that execute human tasks by controlling computers can
enhance human productivity and application accessibility. Yet, progress in this
field will be driven by realistic and reproducible benchmarks. We present
AndroidWorld, a fully functioning Android environment that provides reward
signals for 116 programmatic task workflows across 20 real world Android
applications. Unlike existing interactive environments, which provide a static
test set, AndroidWorld dynamically constructs tasks that are parameterized and
expressed in natural language in unlimited ways, thus enabling testing on a
much larger and realistic suite of tasks. Reward signals are derived from the
computer's system state, making them durable across task variations and
extensible across different apps. To demonstrate AndroidWorld's benefits and
mode of operation, we introduce a new computer control agent, M3A. M3A can
complete 30.6% of the AndroidWorld's tasks, leaving ample room for future work.
Furthermore, we adapt a popular desktop web agent to work on Android, which we
find to be less effective on mobile, suggesting future research is needed to
achieve universal, cross-domain agents. Finally, we conduct a robustness
analysis by testing M3A against a range of task variations on a representative
subset of tasks, demonstrating that variations in task parameters can
significantly alter the complexity of a task and therefore an agent's
performance, highlighting the importance of testing agents under diverse
conditions. AndroidWorld and the experiments in this paper are available at
this https URL

自主代理、AndroidWorld、奖励信号、编程任务工作流、计算机控制代理

AndroidWorld: 一个用于自主智能体动态基准测试的环境

AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents

We study adversarial attacks that manipulate the reward signals to control
the actions chosen by a stochastic multi-armed bandit algorithm. We propose the
first attack against two popular bandit algorithms: $\epsilon$-greedy and UCB,
\emph{without} knowledge of the mean rewards. The attacker is able to spend
only logarithmic effort, multiplied by a problem-specific parameter that
becomes smaller as the bandit problem gets easier to attack. The result means
the attacker can easily hijack the behavior of the bandit algorithm to promote
or obstruct certain actions, say, a particular medical treatment. As bandits
are seeing increasingly wide use in practice, our study exposes a significant
security threat.

该论文研究了对多臂赌博算法进行的对抗攻击，以操纵奖励信号以控制算法选择的行动，并提出了针对常见的两种多臂赌博算法 epsilon-greedy 和 UCB 的攻击方案。这种攻击是在不知道平均奖励的情况下进行的，并且攻击者所需的努力是对问题特定参数取对数，这个参数随着赌博问题变得越来越容易攻击而变小。结果表明，攻击者可以轻松地劫持多臂赌博算法的行为，以推广或阻止某些行动。由于多臂赌博算法在实践中的使用越来越广泛，因此我们的研究揭示了一个重大的安全威胁。