Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation

• URL: http://arxiv.org/abs/2004.03222v2
• Date: Sun, 6 Dec 2020 11:30:14 GMT
• Title: Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation
• Authors: Mahdi Kazemi Moghaddam, Qi Wu, Ehsan Abbasnejad and Javen Qinfeng Shi
• Abstract summary: We argue that this ability is largely due to three main reasons: the incorporation of prior knowledge (or experience), the adaptation of it to the new environment using the observed visual cues and optimistically searching without giving up early. This is currently missing in the state-of-the-art visual navigation methods based on Reinforcement Learning (RL) In this paper, we propose to use externally learned prior knowledge of the relative object locations and integrate it into our model by constructing a neural graph.
• Score: 18.519303422753534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We humans can impeccably search for a target object, given its name only, even in an unseen environment. We argue that this ability is largely due to three main reasons: the incorporation of prior knowledge (or experience), the adaptation of it to the new environment using the observed visual cues and most importantly optimistically searching without giving up early. This is currently missing in the state-of-the-art visual navigation methods based on Reinforcement Learning (RL). In this paper, we propose to use externally learned prior knowledge of the relative object locations and integrate it into our model by constructing a neural graph. In order to efficiently incorporate the graph without increasing the state-space complexity, we propose our Graph-based Value Estimation (GVE) module. GVE provides a more accurate baseline for estimating the Advantage function in actor-critic RL algorithm. This results in reduced value estimation error and, consequently, convergence to a more optimal policy. Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate. Our extensive ablation studies show the efficacy of our simple method which achieves the state-of-the-art results measured by the conventional visual navigation metrics, e.g. Success Rate (SR) and Success weighted by Path Length (SPL), in AI2THOR environment.

Related papers

• Augmented Commonsense Knowledge for Remote Object Grounding [67.30864498454805]
  We propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as atemporal knowledge graph for improving agent navigation. ACK consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment. We add a new pipeline for the commonsense-based decision-making process which leads to more accurate local action prediction.
  arXiv  Detail & Related papers  (2024-06-03T12:12:33Z)
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning [55.96599486604344]
  We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process. We use Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals. The proposed algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this newly generated step-level preference data.
  arXiv  Detail & Related papers  (2024-05-01T11:10:24Z)
• Logical Bias Learning for Object Relation Prediction [3.724255294816294]
  Scene graph generation (SGG) aims to automatically map an image into a semantic structural graph for better scene understanding. It faces severe limitations in practice due to the biased data and training method. We present a more rational and effective strategy based on causal inference for object relation prediction.
  arXiv  Detail & Related papers  (2023-10-01T16:12:00Z)
• INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL [90.06845886194235]
  We propose a modified objective for model-based reinforcement learning (RL) We integrate a term inspired by variational empowerment into a state-space model based on mutual information. We evaluate the approach on a suite of vision-based robot control tasks with natural video backgrounds.
  arXiv  Detail & Related papers  (2022-04-18T23:09:23Z)
• Symmetry-aware Neural Architecture for Embodied Visual Navigation [24.83118298491349]
  Experimental results show that our method increases area coverage by $8.1 m2$ when trained on the Gibson dataset and tested on the MP3D dataset.
  arXiv  Detail & Related papers  (2021-12-17T14:07:23Z)
• Hierarchical Object-to-Zone Graph for Object Navigation [43.558927774552295]
  In the unseen environment, when the target object is not in egocentric view, the agent may not be able to make wise decisions. We propose a hierarchical object-to-zone (HOZ) graph to guide the agent in a coarse-to-fine manner. Online-learning mechanism is also proposed to update HOZ according to the real-time observation in new environments.
  arXiv  Detail & Related papers  (2021-09-05T13:02:17Z)
• Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles [16.45799795374353]
  We propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing. Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% 20% on the state-of-the-art perception algorithms.
  arXiv  Detail & Related papers  (2021-05-02T01:52:18Z)
• RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding [50.010601631982425]
  This paper extends the random walk model (Arora et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) We derive a scoring function that evaluates the strength of a relation R between two entities h (head) and t (tail) We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph.
  arXiv  Detail & Related papers  (2021-01-25T13:31:29Z)
• Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration [143.43658264904863]
  We show how iteration under a more standard notion of low inherent Bellman error, typically employed in least-square value-style algorithms, can provide strong PAC guarantees on learning a near optimal value function. We present a computationally tractable algorithm for the reward-free setting and show how it can be used to learn a near optimal policy for any (linear) reward function.
  arXiv  Detail & Related papers  (2020-08-18T04:34:21Z)
• Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation [0.0]
  This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences.
  arXiv  Detail & Related papers  (2020-02-25T08:04:31Z)
• Value-driven Hindsight Modelling [68.658900923595]
  Value estimation is a critical component of the reinforcement learning (RL) paradigm. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. We develop an approach for representation learning in RL that sits in between these two extremes. This provides tractable prediction targets that are directly relevant for a task, and can thus accelerate learning the value function.
  arXiv  Detail & Related papers  (2020-02-19T18:10:20Z)

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.