A transformer-based deep reinforcement learning approach to spatial navigation in a partially observable Morris Water Maze

• URL: http://arxiv.org/abs/2410.12820v1
• Date: Tue, 01 Oct 2024 13:22:56 GMT
• Title: A transformer-based deep reinforcement learning approach to spatial navigation in a partially observable Morris Water Maze
• Authors: Marte Eggen, Inga Strümke,
• Abstract summary: This work applies a transformer-based architecture using deep reinforcement learning to navigate a 2D version of the Morris Water Maze. We demonstrate that the proposed architecture enables the agent to efficiently learn spatial navigation strategies. This work suggests promising avenues for future research in artificial agents whose behavior resembles that of biological agents.
• Score: 0.0
• License:
• Abstract: Navigation is a fundamental cognitive skill extensively studied in neuroscientific experiments and has lately gained substantial interest in artificial intelligence research. Recreating the task solved by rodents in the well-established Morris Water Maze (MWM) experiment, this work applies a transformer-based architecture using deep reinforcement learning -- an approach previously unexplored in this context -- to navigate a 2D version of the maze. Specifically, the agent leverages a decoder-only transformer architecture serving as a deep Q-network performing effective decision making in the partially observable environment. We demonstrate that the proposed architecture enables the agent to efficiently learn spatial navigation strategies, overcoming challenges associated with a limited field of vision, corresponding to the visual information available to a rodent in the MWM. Demonstrating the potential of transformer-based models for enhancing navigation performance in partially observable environments, this work suggests promising avenues for future research in artificial agents whose behavior resembles that of biological agents. Finally, the flexibility of the transformer architecture in supporting varying input sequence lengths opens opportunities for gaining increased understanding of the artificial agent's inner representation of the environment.

Related papers

• A Role of Environmental Complexity on Representation Learning in Deep Reinforcement Learning Agents [3.7314353481448337]
  We developed a simulated navigation environment to train deep reinforcement learning agents. We modulated the frequency of exposure to a shortcut and navigation cue, leading to the development of artificial agents with differing abilities. We examined the encoded representations in artificial neural networks driving these agents, revealing intricate dynamics in representation learning.
  arXiv  Detail & Related papers  (2024-07-03T18:27:26Z)
• Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers [56.264673865476986]
  This paper introduces Skip-Layer Attention (SLA) to enhance Transformer models. SLA improves the model's ability to capture dependencies between high-level abstract features and low-level details. Our implementation extends the Transformer's functionality by enabling queries in a given layer to interact with keys and values from both the current layer and one preceding layer.
  arXiv  Detail & Related papers  (2024-06-17T07:24:38Z)
• Enhanced Low-Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots Using Double Deep Reinforcement Learning Techniques [1.191504645891765]
  We present two distinct approaches aimed at enhancing mapless navigation for a ground-based mobile robot. The research methodology primarily involves a comparative analysis between a Deep-RL strategy grounded in the foundational Deep Q-Network (DQN) algorithm, and an alternative approach based on the Double Deep Q-Network (DDQN) algorithm. The proposed methodology is evaluated in three different real environments, revealing that Double Deep structures significantly enhance the navigation capabilities of mobile robots compared to simple Q structures.
  arXiv  Detail & Related papers  (2023-10-20T20:47:07Z)
• Learning Navigational Visual Representations with Semantic Map Supervision [85.91625020847358]
  We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps. Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
  arXiv  Detail & Related papers  (2023-07-23T14:01:05Z)
• A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks [60.38369406877899]
  Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. Our survey encompasses the identification of the top five application domains for transformer-based models.
  arXiv  Detail & Related papers  (2023-06-11T23:13:51Z)
• Investigating Navigation Strategies in the Morris Water Maze through Deep Reinforcement Learning [4.408196554639971]
  In this work, we simulate the Morris Water Maze in 2D to train deep reinforcement learning agents. We perform automatic classification of navigation strategies, analyze the distribution of strategies used by artificial agents, and compare them with experimental data to show similar learning dynamics as those seen in humans and rodents.
  arXiv  Detail & Related papers  (2023-06-01T18:16:16Z)
• Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive Imitation [0.0]
  We propose DATI, a deep reinforcement learning agent designed for domain-adaptive trajectory imitation. Our experiments show that DATI outperforms baseline methods for imitation learning and optimal control in this setting. Its generalization to a real-world scenario is shown through the discovery of abnormal motion patterns in maritime traffic.
  arXiv  Detail & Related papers  (2023-04-19T15:53:48Z)
• 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)
• Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation [11.868792440783055]
  We show that learning to estimate metrics quantifying the spatial relationships between an agent at a given location and a goal to reach has a high positive impact in Multi-Object Navigation settings. A learning-based agent from the literature trained with the proposed auxiliary losses was the winning entry to the Multi-Object Navigation Challenge.
  arXiv  Detail & Related papers  (2021-07-13T12:01:05Z)
• Transformers Solve the Limited Receptive Field for Monocular Depth Prediction [82.90445525977904]
  We propose TransDepth, an architecture which benefits from both convolutional neural networks and transformers. This is the first paper which applies transformers into pixel-wise prediction problems involving continuous labels.
  arXiv  Detail & Related papers  (2021-03-22T18:00:13Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

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.