Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the risks of reward shaping, particularly through potential-based reward shaping (PBRS), has not been applicable to many IM methods, as they are often complex, trainable functions themselves, and therefore dependent on a wider set of variables than the traditional reward functions that PBRS was developed for. We present an extension to PBRS that we prove preserves the set of optimal policies under a more general set of functions than has been previously proven. We also present {\em Potential-Based Intrinsic Motivation} (PBIM) and {\em Generalized Reward Matching} (GRM), methods for converting IM rewards into a potential-based form that are useable without altering the set of optimal policies. Testing in the MiniGrid DoorKey and Cliff Walking environments, we demonstrate that PBIM and GRM successfully prevent the agent from converging to a suboptimal policy and can speed up training. Additionally, we prove that GRM is sufficiently general as to encompass all potential-based reward shaping functions. This paper expands on previous work introducing the PBIM method, and provides an extension to the more general method of GRM, as well as additional proofs, experimental results, and discussion.

Collaborative Comic Generation: Integrating Visual Narrative Theories with AI Models for Enhanced Creativity

This study presents a theory-inspired visual narrative generative system that integrates conceptual principles-comic authoring idioms-with generative and language models to enhance the comic creation process. Our system combines human creativity with AI models to support parts of the generative process, providing a collaborative platform for creating comic content. These comic-authoring idioms, derived from prior human-created image sequences, serve as guidelines for crafting and refining storytelling. The system translates these principles into system layers that facilitate comic creation through sequential decision-making, addressing narrative elements such as panel composition, story tension changes, and panel transitions. Key contributions include integrating machine learning models into the human-AI cooperative comic generation process, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences. This approach improves narrative elements in generated image sequences and engages human creativity in an AI-generative process of comics. We open-source the code at https://github.com/RimiChen/Collaborative_Comic_Generation.

Potential-Based Reward Shaping For Intrinsic Motivation

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the risks of reward shaping, particularly through potential-based reward shaping (PBRS), has not been applicable to many IM methods, as they are often complex, trainable functions themselves, and therefore dependent on a wider set of variables than the traditional reward functions that PBRS was developed for. We present an extension to PBRS that we prove preserves the set of optimal policies under a more general set of functions than has been previously proven. We also present {\em Potential-Based Intrinsic Motivation} (PBIM), a method for converting IM rewards into a potential-based form that is useable without altering the set of optimal policies. Testing in the MiniGrid DoorKey and Cliff Walking environments, we demonstrate that PBIM successfully prevents the agent from converging to a suboptimal policy and can speed up training.

Panel Transitions for Genre Analysis in Visual Narratives

Understanding how humans communicate and perceive narratives is important for media technology research and development. This is particularly important in current times when there are tools and algorithms that are easily available for amateur users to create high-quality content. Narrative media develops over time a set of recognizable patterns of features across similar artifacts. Genre is one such grouping of artifacts for narrative media with similar patterns, tropes, and story structures. While much work has been done on genre-based classifications in text and video, we present a novel approach to do a multi-modal analysis of genre based on comics and manga-style visual narratives. We present a systematic feature analysis of an annotated dataset that includes a variety of western and eastern visual books with annotations for high-level narrative patterns. We then present a detailed analysis of the contributions of high-level features to genre classification for this medium. We highlight some of the limitations and challenges of our existing computational approaches in modeling subjective labels. Our contributions to the community are: a dataset of annotated manga books, a multi-modal analysis of visual panels and text in a constrained and popular medium through high-level features, and a systematic process for incorporating subjective narrative patterns in computational models.

CPST: Comprehension-Preserving Style Transfer for Multi-Modal Narratives

We investigate the challenges of style transfer in multi-modal visual narratives. Among static visual narratives such as comics and manga, there are distinct visual styles in terms of presentation. They include style features across multiple dimensions, such as panel layout, size, shape, and color. They include both visual and text media elements. The layout of both text and media elements is also significant in terms of narrative communication. The sequential transitions between panels are where readers make inferences about the narrative world. These feature differences provide an interesting challenge for style transfer in which there are distinctions between the processing of features for each modality. We introduce the notion of comprehension-preserving style transfer (CPST) in such multi-modal domains. CPST requires not only traditional metrics of style transfer but also metrics of narrative comprehension. To spur further research in this area, we present an annotated dataset of comics and manga and an initial set of algorithms that utilize separate style transfer modules for the visual, textual, and layout parameters. To test whether the style transfer preserves narrative semantics, we evaluate this algorithm through visual story cloze tests inspired by work in computational cognition of narrative systems. Understanding the connection between style and narrative semantics provides insight for applications ranging from informational brochure designs to data storytelling.

Modeling Risk in Reinforcement Learning: A Literature Mapping

Safe reinforcement learning deals with mitigating or avoiding unsafe situations by reinforcement learning (RL) agents. Safe RL approaches are based on specific risk representations for particular problems or domains. In order to analyze agent behaviors, compare safe RL approaches, and effectively transfer techniques between application domains, it is necessary to understand the types of risk specific to safe RL problems. We performed a systematic literature mapping with the objective to characterize risk in safe RL. Based on the obtained results, we present definitions, characteristics, and types of risk that hold on multiple application domains. Our literature mapping covers literature from the last 5 years (2017-2022), from a variety of knowledge areas (AI, finance, engineering, medicine) where RL approaches emphasize risk representation and management. Our mapping covers 72 papers filtered systematically from over thousands of papers on the topic. Our proposed notion of risk covers a variety of representations, disciplinary differences, common training exercises, and types of techniques. We encourage researchers to include explicit and detailed accounts of risk in future safe RL research reports, using this mapping as a starting point. With this information, researchers and practitioners could draw stronger conclusions on the effectiveness of techniques on different problems.

Emergent social NPC interactions in the Social NPCs Skyrim mod and beyond

This work presents an implementation of a social architecture model for authoring Non-Player Character (NPC) in open world games inspired in academic research on agentbased modeling. Believable NPC authoring is burdensome in terms of rich dialogue and responsive behaviors. We briefly present the characteristics and advantages of using a social agent architecture for this task and describe an implementation of a social agent architecture CiF-CK released as a mod Social NPCs for The Elder Scrolls V: Skyrim