Episodic Memory for Learning Subjective-Timescale Models

• URL: http://arxiv.org/abs/2010.01430v1
• Date: Sat, 3 Oct 2020 21:55:40 GMT
• Title: Episodic Memory for Learning Subjective-Timescale Models
• Authors: Alexey Zakharov, Matthew Crosby, Zafeirios Fountas
• Abstract summary: In model-based learning, an agent's model is commonly defined over transitions between consecutive states of an environment. In contrast, intelligent behaviour in biological organisms is characterised by the ability to plan over varying temporal scales depending on the context. We devise a novel approach to learning a transition dynamics model, based on the sequences of episodic memories that define the agent's subjective timescale.
• Score: 1.933681537640272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In model-based learning, an agent's model is commonly defined over transitions between consecutive states of an environment even though planning often requires reasoning over multi-step timescales, with intermediate states either unnecessary, or worse, accumulating prediction error. In contrast, intelligent behaviour in biological organisms is characterised by the ability to plan over varying temporal scales depending on the context. Inspired by the recent works on human time perception, we devise a novel approach to learning a transition dynamics model, based on the sequences of episodic memories that define the agent's subjective timescale - over which it learns world dynamics and over which future planning is performed. We implement this in the framework of active inference and demonstrate that the resulting subjective-timescale model (STM) can systematically vary the temporal extent of its predictions while preserving the same computational efficiency. Additionally, we show that STM predictions are more likely to introduce future salient events (for example new objects coming into view), incentivising exploration of new areas of the environment. As a result, STM produces more informative action-conditioned roll-outs that assist the agent in making better decisions. We validate significant improvement in our STM agent's performance in the Animal-AI environment against a baseline system, trained using the environment's objective-timescale dynamics.

Related papers

• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• Disentangled Neural Relational Inference for Interpretable Motion Prediction [38.40799770648501]
  We develop a variational auto-encoder framework that integrates graph-based representations and timesequence models. Our model infers dynamic interaction graphs augmented with interpretable edge features that characterize the interactions. We validate our approach through extensive experiments on both simulated and real-world datasets.
  arXiv  Detail & Related papers  (2024-01-07T22:49:24Z)
• OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning [67.07363529640784]
  We propose OpenSTL to categorize prevalent approaches into recurrent-based and recurrent-free models. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and forecasting weather. We find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models.
  arXiv  Detail & Related papers  (2023-06-20T03:02:14Z)
• Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation [66.86987509942607]
  We evaluate how such a paradigm should be done in imitation learning. We consider a setting where the pretraining corpus consists of multitask demonstrations. We argue that inverse dynamics modeling is well-suited to this setting.
  arXiv  Detail & Related papers  (2023-05-26T14:40:46Z)
• Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark [49.58762201363483]
  We introduce RatPose, a bio-robot motion prediction dataset constructed by considering the influence factors of individuals and environments. We propose a Dual-stream Motion-Scenario Decoupling framework that effectively separates scenario-oriented and motion-oriented features. We demonstrate significant performance improvements of the proposed textitDMSD framework on different difficulty-level tasks.
  arXiv  Detail & Related papers  (2023-05-17T14:14:31Z)
• Predictive Experience Replay for Continual Visual Control and Forecasting [62.06183102362871]
  We present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. We first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:08:03Z)
• Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models [2.7391842773173334]
  We develop a simulation-based deep learning method for Bayesian inference, which can recover both time-varying and time-invariant parameters. Our results show that the deep learning approach is very efficient in capturing the temporal dynamics of the model.
  arXiv  Detail & Related papers  (2022-11-23T17:42:53Z)
• Multi-Timescale Modeling of Human Behavior [0.18199355648379031]
  We propose an LSTM network architecture that processes behavioral information at multiple timescales to predict future behavior. We evaluate our architecture on data collected in an urban search and rescue scenario simulated in a virtual Minecraft-based testbed.
  arXiv  Detail & Related papers  (2022-11-16T15:58:57Z)
• Variational Predictive Routing with Nested Subjective Timescales [1.6114012813668934]
  We present Variational Predictive Routing (PRV) - a neural inference system that organizes latent video features in a temporal hierarchy. We show that VPR is able to detect event boundaries, disentangletemporal features, adapt to the dynamics hierarchy of the data, and produce accurate time-agnostic rollouts of the future.
  arXiv  Detail & Related papers  (2021-10-21T16:12:59Z)
• Temporal Predictive Coding For Model-Based Planning In Latent Space [80.99554006174093]
  We present an information-theoretic approach that employs temporal predictive coding to encode elements in the environment that can be predicted across time. We evaluate our model on a challenging modification of standard DMControl tasks where the background is replaced with natural videos that contain complex but irrelevant information to the planning task.
  arXiv  Detail & Related papers  (2021-06-14T04:31:15Z)
• Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach [6.1938383008964495]
  This study develops an activity-based modeling framework for individual mobility prediction. We show that the proposed model can achieve similar prediction performance as the state-of-the-art long-term short-term memory (LSTM) model.
  arXiv  Detail & Related papers  (2021-01-11T16:11:27Z)

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.