Matrix Low-Rank Approximation For Policy Gradient Methods

• URL: http://arxiv.org/abs/2405.17626v1
• Date: Mon, 27 May 2024 19:49:08 GMT
• Title: Matrix Low-Rank Approximation For Policy Gradient Methods
• Authors: Sergio Rozada, Antonio G. Marques,
• Abstract summary: Estimating a policy that maps to actions is a central problem in reinforcement learning. In this paper, we put forth low-rank matrix-based models to estimate efficiently the parameters of gradient Policy algorithms.
• Score: 10.196333441334895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating a policy that maps states to actions is a central problem in reinforcement learning. Traditionally, policies are inferred from the so called value functions (VFs), but exact VF computation suffers from the curse of dimensionality. Policy gradient (PG) methods bypass this by learning directly a parametric stochastic policy. Typically, the parameters of the policy are estimated using neural networks (NNs) tuned via stochastic gradient descent. However, finding adequate NN architectures can be challenging, and convergence issues are common as well. In this paper, we put forth low-rank matrix-based models to estimate efficiently the parameters of PG algorithms. We collect the parameters of the stochastic policy into a matrix, and then, we leverage matrix-completion techniques to promote (enforce) low rank. We demonstrate via numerical studies how low-rank matrix-based policy models reduce the computational and sample complexities relative to NN models, while achieving a similar aggregated reward.

Related papers

• Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models [56.92178753201331]
  We tackle average-reward infinite-horizon POMDPs with an unknown transition model. We present a novel and simple estimator that overcomes this barrier.
  arXiv  Detail & Related papers  (2025-01-30T22:29:41Z)
• Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning [27.868175900131313]
  Reinforcement learning (RL) aims to estimate the action to take given a (time-varying) state. This paper postulates multi-linear mappings to efficiently estimate the parameters of the RL policy. We leverage the PARAFAC decomposition to design tensor low-rank policies.
  arXiv  Detail & Related papers  (2025-01-08T23:22:08Z)
• Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation [48.92318828548911]
  We present LoRa-PI (Low-Rank Policy Iteration), a model-free learning algorithm alternating between policy improvement and policy evaluation steps. LoRa-PI learns an $varepsilon$-optimal policy using $widetildeO(S+Aover mathrmpoly (1-gamma)varepsilon2)$ samples where $S$ denotes the number of states (resp. actions) and $gamma$ the discount factor.
  arXiv  Detail & Related papers  (2024-10-30T20:22:17Z)
• Matrix Low-Rank Trust Region Policy Optimization [10.196333441334895]
  Methods in reinforcement learning use a Policy Gradient approach to learn a parametric policy that maps to actions. Trust region algorithms, like Trust Region Policy Optimization (TRPO), constrain the policy update step, ensuring monotonic improvements. This paper introduces low-rank matrix-based models as an efficient alternative for estimating the parameters of TRPO algorithms.
  arXiv  Detail & Related papers  (2024-05-27T19:46:31Z)
• Learning Optimal Deterministic Policies with Stochastic Policy Gradients [62.81324245896716]
  Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. In common practice, convergence (hyper)policies are learned only to deploy their deterministic version. We show how to tune the exploration level used for learning to optimize the trade-off between the sample complexity and the performance of the deployed deterministic policy.
  arXiv  Detail & Related papers  (2024-05-03T16:45:15Z)
• Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems [1.747623282473278]
  We introduce a policygradient method for model reinforcement learning (RL) that exploits a type of stationary distributions commonly obtained from decision processes (MDPs) in networks. Specifically, when the stationary distribution of the MDP is parametrized by policy parameters, we can improve existing policy methods for average-reward estimation.
  arXiv  Detail & Related papers  (2023-12-05T14:44:58Z)
• Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms [1.776746672434207]
  We study policy gradient (PG) for reinforcement learning in continuous time and space. We propose two types of the actor-critic algorithms for RL, where we learn and update value functions and policies simultaneously and alternatingly.
  arXiv  Detail & Related papers  (2021-11-22T14:27:04Z)
• Average-Reward Off-Policy Policy Evaluation with Function Approximation [66.67075551933438]
  We consider off-policy policy evaluation with function approximation in average-reward MDPs. bootstrapping is necessary and, along with off-policy learning and FA, results in the deadly triad. We propose two novel algorithms, reproducing the celebrated success of Gradient TD algorithms in the average-reward setting.
  arXiv  Detail & Related papers  (2021-01-08T00:43:04Z)
• Deep Bayesian Quadrature Policy Optimization [100.81242753620597]
  Deep Bayesian quadrature policy gradient (DBQPG) is a high-dimensional generalization of Bayesian quadrature for policy gradient estimation. We show that DBQPG can substitute Monte-Carlo estimation in policy gradient methods, and demonstrate its effectiveness on a set of continuous control benchmarks.
  arXiv  Detail & Related papers  (2020-06-28T15:44:47Z)
• Stochastic Recursive Momentum for Policy Gradient Methods [28.277961340108313]
  We propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (Storm-PG) Storm-PG enjoys a provably sharp $O (1/epsilon3)$ sample bound for STORM-PG, matching the best-known convergence rate for policy gradient algorithm. Numerical experiments depicts the superiority of our algorithm over comparative policy gradient algorithms.
  arXiv  Detail & Related papers  (2020-03-09T17:59:03Z)
• Efficient Policy Learning from Surrogate-Loss Classification Reductions [65.91730154730905]
  We consider the estimation problem given by a weighted surrogate-loss classification reduction of policy learning. We show that, under a correct specification assumption, the weighted classification formulation need not be efficient for policy parameters. We propose an estimation approach based on generalized method of moments, which is efficient for the policy parameters.
  arXiv  Detail & Related papers  (2020-02-12T18:54:41Z)

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.