441 research outputs found
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
The capability of reinforcement learning (RL) agent directly depends on the
diversity of learning scenarios the environment generates and how closely it
captures real-world situations. However, existing environments/simulators lack
the support to systematically model distributions over initial states and
transition dynamics. Furthermore, in complex domains such as soccer, the space
of possible scenarios is infinite, which makes it impossible for one research
group to provide a comprehensive set of scenarios to train, test, and benchmark
RL algorithms. To address this issue, for the first time, we adopt an existing
formal scenario specification language, SCENIC, to intuitively model and
generate interactive scenarios. We interfaced SCENIC to Google Research Soccer
environment to create a platform called SCENIC4RL. Using this platform, we
provide a dataset consisting of 36 scenario programs encoded in SCENIC and
demonstration data generated from a subset of them. We share our experimental
results to show the effectiveness of our dataset and the platform to train,
test, and benchmark RL algorithms. More importantly, we open-source our
platform to enable RL community to collectively contribute to constructing a
comprehensive set of scenarios.Comment: First two authors contributed equally. Currently Under Revie
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
Data-driven simulation has become a favorable way to train and test
autonomous driving algorithms. The idea of replacing the actual environment
with a learned simulator has also been explored in model-based reinforcement
learning in the context of world models. In this work, we show data-driven
traffic simulation can be formulated as a world model. We present TrafficBots,
a multi-agent policy built upon motion prediction and end-to-end driving, and
based on TrafficBots we obtain a world model tailored for the planning module
of autonomous vehicles. Existing data-driven traffic simulators are lacking
configurability and scalability. To generate configurable behaviors, for each
agent we introduce a destination as navigational information, and a
time-invariant latent personality that specifies the behavioral style. To
improve the scalability, we present a new scheme of positional encoding for
angles, allowing all agents to share the same vectorized context and the use of
an architecture based on dot-product attention. As a result, we can simulate
all traffic participants seen in dense urban scenarios. Experiments on the
Waymo open motion dataset show TrafficBots can simulate realistic multi-agent
behaviors and achieve good performance on the motion prediction task.Comment: Published at ICRA 2023. The repository is available at
https://github.com/zhejz/TrafficBot
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Towards Informed Exploration for Deep Reinforcement Learning
In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact
Types of Bots: Categorization of Accounts Using Unsupervised Machine Learning
abstract: Social media bot detection has been a signature challenge in recent years in online social networks. Many scholars agree that the bot detection problem has become an "arms race" between malicious actors, who seek to create bots to influence opinion on these networks, and the social media platforms to remove these accounts. Despite this acknowledged issue, bot presence continues to remain on social media networks. So, it has now become necessary to monitor different bots over time to identify changes in their activities or domain. Since monitoring individual accounts is not feasible, because the bots may get suspended or deleted, bots should be observed in smaller groups, based on their characteristics, as types. Yet, most of the existing research on social media bot detection is focused on labeling bot accounts by only distinguishing them from human accounts and may ignore differences between individual bot accounts. The consideration of these bots' types may be the best solution for researchers and social media companies alike as it is in both of their best interests to study these types separately. However, up until this point, bot categorization has only been theorized or done manually. Thus, the goal of this research is to automate this process of grouping bots by their respective types. To accomplish this goal, the author experimentally demonstrates that it is possible to use unsupervised machine learning to categorize bots into types based on the proposed typology by creating an aggregated dataset, subsequent to determining that the accounts within are bots, and utilizing an existing typology for bots. Having the ability to differentiate between types of bots automatically will allow social media experts to analyze bot activity, from a new perspective, on a more granular level. This way, researchers can identify patterns related to a given bot type's behaviors over time and determine if certain detection methods are more viable for that type.Dissertation/ThesisPresentation Materials for Thesis DefenseMasters Thesis Computer Science 201
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