QT-TDM: Planning with Transformer Dynamics Model and Autoregressive Q-Learning

• URL: http://arxiv.org/abs/2407.18841v1
• Date: Fri, 26 Jul 2024 16:05:26 GMT
• Title: QT-TDM: Planning with Transformer Dynamics Model and Autoregressive Q-Learning
• Authors: Mostafa Kotb, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter,
• Abstract summary: We investigate the use of Transformers in Reinforcement Learning (RL) We learn an autoregressive discrete Q-function using a separate Q-Transformer model to estimate a long-term return beyond the short-horizon planning. Our proposed method, QT-TDM, integrates the robust predictive capabilities of Transformers as dynamics models with the efficacy of a model-free Q-Transformer to mitigate the computational burden associated with real-time planning.
• Score: 17.914580097058106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inspired by the success of the Transformer architecture in natural language processing and computer vision, we investigate the use of Transformers in Reinforcement Learning (RL), specifically in modeling the environment's dynamics using Transformer Dynamics Models (TDMs). We evaluate the capabilities of TDMs for continuous control in real-time planning scenarios with Model Predictive Control (MPC). While Transformers excel in long-horizon prediction, their tokenization mechanism and autoregressive nature lead to costly planning over long horizons, especially as the environment's dimensionality increases. To alleviate this issue, we use a TDM for short-term planning, and learn an autoregressive discrete Q-function using a separate Q-Transformer (QT) model to estimate a long-term return beyond the short-horizon planning. Our proposed method, QT-TDM, integrates the robust predictive capabilities of Transformers as dynamics models with the efficacy of a model-free Q-Transformer to mitigate the computational burden associated with real-time planning. Experiments in diverse state-based continuous control tasks show that QT-TDM is superior in performance and sample efficiency compared to existing Transformer-based RL models while achieving fast and computationally efficient inference.

Related papers

• PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting [82.03373838627606]
  Self-attention mechanism in Transformer architecture requires positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positional embeddings restricts the Transformer's ability to effectively represent temporal sequences. We present a model integrating PRE with a standard Transformer encoder, demonstrating state-of-the-art performance on various real-world datasets.
  arXiv  Detail & Related papers  (2024-08-20T01:56:07Z)
• Q-value Regularized Transformer for Offline Reinforcement Learning [70.13643741130899]
  We propose a Q-value regularized Transformer (QT) to enhance the state-of-the-art in offline reinforcement learning (RL) QT learns an action-value function and integrates a term maximizing action-values into the training loss of Conditional Sequence Modeling (CSM) Empirical evaluations on D4RL benchmark datasets demonstrate the superiority of QT over traditional DP and CSM methods.
  arXiv  Detail & Related papers  (2024-05-27T12:12:39Z)
• Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers [10.566264033360282]
  Post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile and TVs. In this paper, we propose a novel PTQ algorithm that balances accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-02-14T05:58:43Z)
• Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting [0.0]
  Transformer models have the potential to improve Load forecasting because of their ability to learn long-range dependencies derived from their Attention Mechanism. Our work compares the proposed Transformer based Neural Network model integrated with different metaheuristic algorithms by their performance in Load forecasting based on numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE)
  arXiv  Detail & Related papers  (2023-07-28T04:29:53Z)
• Efficient GPT Model Pre-training using Tensor Train Matrix Representation [65.96485282393361]
  Large-scale transformer models feature billions of parameters, leading to difficulties in their deployment and prohibitive training costs from scratch. To reduce the number of parameters in the GPT-2 architecture, we replace the matrices of fully-connected layers with the corresponding Train Matrix(TTM) structure. The resulting GPT-based model stores up to 40% fewer parameters, showing the perplexity comparable to the original model.
  arXiv  Detail & Related papers  (2023-06-05T08:38:25Z)
• Emergent Agentic Transformer from Chain of Hindsight Experience [96.56164427726203]
  We show that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches. This is the first time that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches.
  arXiv  Detail & Related papers  (2023-05-26T00:43:02Z)
• Continuous Spatiotemporal Transformers [2.485182034310304]
  We present the Continuous Stemporal Transformer (CST), a new transformer architecture that is designed to modeling continuous systems. This new framework guarantees a continuous representation and output via optimization in Sobolev space. We benchmark CST against traditional transformers as well as other smoothtemporal dynamics modeling methods and achieve superior performance in a number of tasks on synthetic and real systems.
  arXiv  Detail & Related papers  (2023-01-31T00:06:56Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• How Crucial is Transformer in Decision Transformer? [29.228813063916206]
  Decision Transformer (DT) is a recently proposed architecture for Reinforcement Learning that frames the decision-making process as an auto-regressive sequence modeling problem. We analyze how crucial the Transformer model is in the complete DT architecture on continuous control tasks.
  arXiv  Detail & Related papers  (2022-11-26T20:13:22Z)
• Decision Transformer: Reinforcement Learning via Sequence Modeling [102.86873656751489]
  We present a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. We present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling. Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.
  arXiv  Detail & Related papers  (2021-06-02T17:53:39Z)

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.