PID Accelerated Temporal Difference Algorithms

• URL: http://arxiv.org/abs/2407.08803v2
• Date: Tue, 3 Sep 2024 16:59:07 GMT
• Title: PID Accelerated Temporal Difference Algorithms
• Authors: Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand,
• Abstract summary: Algorithms such as Value Iteration and Temporal Difference (TD) learning have a slow convergence rate and become inefficient in these tasks. PID VI was recently introduced to accelerate the convergence of Value Iteration using ideas from control theory. We give a theoretical analysis of the convergence of PID TD Learning and its acceleration compared to the conventional TD Learning.
• Score: 7.634360142922117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-horizon tasks, which have a large discount factor, pose a challenge for most conventional reinforcement learning (RL) algorithms. Algorithms such as Value Iteration and Temporal Difference (TD) learning have a slow convergence rate and become inefficient in these tasks. When the transition distributions are given, PID VI was recently introduced to accelerate the convergence of Value Iteration using ideas from control theory. Inspired by this, we introduce PID TD Learning and PID Q-Learning algorithms for the RL setting, in which only samples from the environment are available. We give a theoretical analysis of the convergence of PID TD Learning and its acceleration compared to the conventional TD Learning. We also introduce a method for adapting PID gains in the presence of noise and empirically verify its effectiveness.

Related papers

• Variational Delayed Policy Optimization [25.668512485348952]
  In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning (RL) State-of-the-art (SOTA) RL techniques with Temporal-Difference (TD) learning frameworks often suffer from learning inefficiency, due to the significant expansion of the augmented state space with the delay. This work introduces a novel framework called Variational Delayed Policy Optimization (VDPO), which reformulates delayed RL as a variational inference problem.
  arXiv  Detail & Related papers  (2024-05-23T06:57:04Z)
• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)
• Efficient Deep Reinforcement Learning Requires Regulating Overfitting [91.88004732618381]
  We show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.
  arXiv  Detail & Related papers  (2023-04-20T17:11:05Z)
• Backstepping Temporal Difference Learning [3.5823366350053325]
  We propose a new convergent algorithm for off-policy TD-learning. Our method relies on the backstepping technique, which is widely used in nonlinear control theory. convergence of the proposed algorithm is experimentally verified in environments where the standard TD-learning is known to be unstable.
  arXiv  Detail & Related papers  (2023-02-20T10:06:49Z)
• Faster Adaptive Federated Learning [84.38913517122619]
  Federated learning has attracted increasing attention with the emergence of distributed data. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
  arXiv  Detail & Related papers  (2022-12-02T05:07:50Z)
• Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning [92.18524491615548]
  Contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL) We study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. Under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
  arXiv  Detail & Related papers  (2022-07-29T17:29:08Z)
• Online Attentive Kernel-Based Temporal Difference Learning [13.94346725929798]
  Online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. Online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference. We propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization.
  arXiv  Detail & Related papers  (2022-01-22T14:47:10Z)
• Cooperative Deep $Q$-learning Framework for Environments Providing Image Feedback [5.607676459156789]
  We address two key challenges in deep reinforcement learning setting, sample inefficiency and slow learning, with a dual NN-driven learning approach. In particular, we develop a temporal difference (TD) error-driven learning approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We show that the proposed methods enables faster learning and convergence and requires reduced buffer size.
  arXiv  Detail & Related papers  (2021-10-28T17:12:41Z)
• Learning Sampling Policy for Faster Derivative Free Optimization [100.27518340593284]
  We propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. Our results show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios.
  arXiv  Detail & Related papers  (2021-04-09T14:50:59Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)

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.