GAN Based Top-Down View Synthesis in Reinforcement Learning Environments

• URL: http://arxiv.org/abs/2410.12372v1
• Date: Wed, 16 Oct 2024 08:44:23 GMT
• Title: GAN Based Top-Down View Synthesis in Reinforcement Learning Environments
• Abstract summary: This project explores learning the top-down view of an RL environment based on the artificial agent's first-person view observations with a generative adversarial network(GAN) The focus of the project is to learn the top-down view of an RL environment. It doesn't deal with any Reinforcement Learning task.
• Score: 0.36427222462368697
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human actions are based on the mental perception of the environment. Even when all the aspects of an environment are not visible, humans have an internal mental model that can generalize the partially visible scenes to fully constructed and connected views. This internal mental model uses learned abstract representations of spatial and temporal aspects of the environments encountered in the past. Artificial agents in reinforcement learning environments also benefit by learning a representation of the environment from experience. It provides the agent with viewpoints that are not directly visible to it, helping it make better policy decisions. It can also be used to predict the future state of the environment. This project explores learning the top-down view of an RL environment based on the artificial agent's first-person view observations with a generative adversarial network(GAN). The top-down view is useful as it provides a complete overview of the environment by building a map of the entire environment. It provides information about the objects' dimensions and shapes along with their relative positions with one another. Initially, when only a partial observation of the environment is visible to the agent, only a partial top-down view is generated. As the agent explores the environment through a set of actions, the generated top-down view becomes complete. This generated top-down view can assist the agent in deducing better policy decisions. The focus of the project is to learn the top-down view of an RL environment. It doesn't deal with any Reinforcement Learning task.

Related papers

• Autonomous Continual Learning of Computer-Use Agents for Environment Adaptation [57.65688895630163]
  We introduce ACuRL, an Autonomous Curriculum Reinforcement Learning framework that continually adapts agents to specific environments with zero human data.<n>Our method effectively enables both intra-environment and cross-environment continual learning, yielding 4-22% performance gains without forgetting existing environments.
  arXiv  Detail & Related papers  (2026-02-10T23:06:02Z)
• Capturing Visual Environment Structure Correlates with Control Performance [62.69063991419694]
  Existing proxy metrics focus on the representation's capacity to capture narrow aspects of the visual world.<n>We measure how well pretrained visual encoders support decoding of environment state.<n>We show that this probing accuracy strongly correlates with downstream policy performance.
  arXiv  Detail & Related papers  (2026-02-04T18:59:12Z)
• Caution for the Environment: Multimodal Agents are Susceptible to Environmental Distractions [68.92637077909693]
  This paper investigates the faithfulness of multimodal large language model (MLLM) agents in a graphical user interface (GUI) environment.<n>A general scenario is proposed where both the user and the agent are benign, and the environment, while not malicious, contains unrelated content.<n> Experimental results reveal that even the most powerful models, whether generalist agents or specialist GUI agents, are susceptible to distractions.
  arXiv  Detail & Related papers  (2024-08-05T15:16:22Z)
• ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environments [13.988804095409133]
  We propose the ReALFRED benchmark that employs real-world scenes, objects, and room layouts to learn agents to complete household tasks. Specifically, we extend the ALFRED benchmark with updates for larger environmental spaces with smaller visual domain gaps. With ReALFRED, we analyze previously crafted methods for the ALFRED benchmark and observe that they consistently yield lower performance in all metrics.
  arXiv  Detail & Related papers  (2024-07-26T07:00:27Z)
• A Landmark-Aware Visual Navigation Dataset [6.564789361460195]
  We present a Landmark-Aware Visual Navigation dataset to allow for supervised learning of human-centric exploration policies and map building. We collect RGB observation and human point-click pairs as a human annotator explores virtual and real-world environments. Our dataset covers a wide spectrum of scenes, including rooms in indoor environments, as well as walkways outdoors.
  arXiv  Detail & Related papers  (2024-02-22T04:43:20Z)
• Selective Visual Representations Improve Convergence and Generalization for Embodied AI [44.33711781750707]
  Embodied AI models often employ off the shelf vision backbones like CLIP to encode their visual observations. This introduces noise within the learning process and distracts the agent's focus from task-relevant visual cues. Inspired by selective attention in humans-the process through which people filter their perception based on their experiences, knowledge, and the task at hand-we introduce a parameter-efficient approach to filter visual stimuli for embodied AI.
  arXiv  Detail & Related papers  (2023-11-07T18:34:02Z)
• OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments [20.034972354302788]
  We extend the Atari Learning Environments, the most-used evaluation framework for deep RL approaches, by introducing OCAtari. Our framework allows for object discovery, object representation learning, as well as object-centric RL.
  arXiv  Detail & Related papers  (2023-06-14T17:28:46Z)
• CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations [98.30038910061894]
  Vision-and-Language Navigation (VLN) tasks require an agent to navigate through the environment based on language instructions. We propose CLEAR: Cross-Lingual and Environment-Agnostic Representations. Our language and visual representations can be successfully transferred to the Room-to-Room and Cooperative Vision-and-Dialogue Navigation task.
  arXiv  Detail & Related papers  (2022-07-05T17:38:59Z)
• What do navigation agents learn about their environment? [39.74076893981299]
  We introduce the Interpretability System for Embodied agEnts (iSEE) for Point Goal and Object Goal navigation agents. We use iSEE to probe the dynamic representations produced by these agents for the presence of information about the agent as well as the environment.
  arXiv  Detail & Related papers  (2022-06-17T01:33:43Z)
• Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams [64.82800502603138]
  This paper proposes a novel neural-network-based approach to progressively and autonomously develop pixel-wise representations in a video stream. The proposed method is based on a human-like attention mechanism that allows the agent to learn by observing what is moving in the attended locations. Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream.
  arXiv  Detail & Related papers  (2022-04-26T09:52:31Z)
• Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
  We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model. This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
  arXiv  Detail & Related papers  (2021-12-07T18:50:42Z)
• Latent World Models For Intrinsically Motivated Exploration [140.21871701134626]
  We present a self-supervised representation learning method for image-based observations. We consider episodic and life-long uncertainties to guide the exploration of partially observable environments.
  arXiv  Detail & Related papers  (2020-10-05T19:47:04Z)
• Diagnosing the Environment Bias in Vision-and-Language Navigation [102.02103792590076]
  Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. Recent works that study VLN observe a significant performance drop when tested on unseen environments, indicating that the neural agent models are highly biased towards training environments. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias.
  arXiv  Detail & Related papers  (2020-05-06T19:24:33Z)
• Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [109.0016923028653]
  We learn point cloud representation by bidirectional reasoning between the local structures and the global shape without human supervision. We show that our unsupervised model surpasses the state-of-the-art supervised methods on both synthetic and real-world 3D object classification datasets.
  arXiv  Detail & Related papers  (2020-03-29T08:26:08Z)
• Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling [65.99956848461915]
  Vision-and-Language Navigation (VLN) is a task where agents must decide how to move through a 3D environment to reach a goal. One of the problems of the VLN task is data scarcity since it is difficult to collect enough navigation paths with human-annotated instructions for interactive environments. We propose an adversarial-driven counterfactual reasoning model that can consider effective conditions instead of low-quality augmented data.
  arXiv  Detail & Related papers  (2019-11-17T18:02:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.