Towards Using Fully Observable Policies for POMDPs

• URL: http://arxiv.org/abs/2207.11737v1
• Date: Sun, 24 Jul 2022 13:22:13 GMT
• Title: Towards Using Fully Observable Policies for POMDPs
• Authors: Andr\'as Attila Sulyok and Krist\'of Karacs
• Abstract summary: Partially Observable Markov Decision Process (POMDP) is a framework applicable to many real world problems. We propose an approach to solve POMDPs with multimodal belief by relying on a policy that solves the fully observable version.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Partially Observable Markov Decision Process (POMDP) is a framework applicable to many real world problems. In this work, we propose an approach to solve POMDPs with multimodal belief by relying on a policy that solves the fully observable version. By defininig a new, mixture value function based on the value function from the fully observable variant, we can use the corresponding greedy policy to solve the POMDP itself. We develop the mathematical framework necessary for discussion, and introduce a benchmark built on the task of Reconnaissance Blind TicTacToe. On this benchmark, we show that our policy outperforms policies ignoring the existence of multiple modes.

Related papers

• Policy Gradient for Robust Markov Decision Processes [16.281897051782863]
  This paper introduces a novel policy gradient method, Double-Loop Robust Policy Mirror Descent (MD), for solving robust Markov Decision Processes (MDPs) MD employs a general mirror descent update rule for the policy optimization with adaptive tolerance per iteration, guaranteeing convergence to a globally optimal policy. We provide a comprehensive analysis of MD, including new convergence results under both direct and softmax parameterizations, and provide novel insights into the inner problem solution through Transition Mirror Ascent (TMA)
  arXiv  Detail & Related papers  (2024-10-29T15:16:02Z)
• MDP Geometry, Normalization and Reward Balancing Solvers [15.627546283580166]
  The Markov Decision Process (MDP) is a widely used mathematical model for sequential decision-making problems. We present a new geometric interpretation of MDPs with a natural normalization procedure that allows us to adjust the value function at each state without altering the advantage of any action with respect to any policy.
  arXiv  Detail & Related papers  (2024-07-09T09:39:45Z)
• On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes [50.68789924454235]
  We present the first finite time global convergence analysis of policy gradient in the context of average reward Markov decision processes (MDPs) Our analysis shows that the policy gradient iterates converge to the optimal policy at a sublinear rate of $Oleft(frac1Tright),$ which translates to $Oleft(log(T)right)$ regret, where $T$ represents the number of iterations.
  arXiv  Detail & Related papers  (2024-03-11T15:25:03Z)
• A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees [5.250288418639076]
  We propose an online POMDP solver called Lazy Cross-Entropy Search Over Policy Trees (LCEOPT) At each planning step, our method uses a novel lazy Cross-Entropy method to search the space of policy trees. Our method is surprisingly simple as compared to existing state-of-the-art methods, yet empirically outperforms them on several continuous-action POMDP problems.
  arXiv  Detail & Related papers  (2023-05-14T03:12:53Z)
• Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning [139.53668999720605]
  We present a multi-agent PPO algorithm in which the local policy of each agent is updated similarly to vanilla PPO. We prove that with standard regularity conditions on the Markov game and problem-dependent quantities, our algorithm converges to the globally optimal policy at a sublinear rate.
  arXiv  Detail & Related papers  (2023-05-08T16:20:03Z)
• Policy Dispersion in Non-Markovian Environment [53.05904889617441]
  This paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment. We first adopt a transformer-based method to learn policy embeddings. Then, we stack the policy embeddings to construct a dispersion matrix to induce a set of diverse policies.
  arXiv  Detail & Related papers  (2023-02-28T11:58:39Z)
• Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems [97.12538243736705]
  We study Reinforcement Learning for partially observable dynamical systems using function approximation. We propose a new textitPartially Observable Bilinear Actor-Critic framework, that is general enough to include models such as POMDPs, observable Linear-Quadratic-Gaussian (LQG), Predictive State Representations (PSRs), as well as a newly introduced model Hilbert Space Embeddings of POMDPs and observable POMDPs with latent low-rank transition.
  arXiv  Detail & Related papers  (2022-06-24T00:27:42Z)
• Efficient Policy Iteration for Robust Markov Decision Processes via Regularization [49.05403412954533]
  Robust decision processes (MDPs) provide a framework to model decision problems where the system dynamics are changing or only partially known. Recent work established the equivalence between texttts rectangular $L_p$ robust MDPs and regularized MDPs, and derived a regularized policy iteration scheme that enjoys the same level of efficiency as standard MDPs. In this work, we focus on the policy improvement step and derive concrete forms for the greedy policy and the optimal robust Bellman operators.
  arXiv  Detail & Related papers  (2022-05-28T04:05:20Z)
• Strengthening Deterministic Policies for POMDPs [5.092711491848192]
  We provide a novel MILP encoding that supports sophisticated specifications in the form of temporal logic constraints. We employ a preprocessing of the POMDP to encompass memory-based decisions. The advantages of our approach lie (1) in the flexibility to strengthen simple deterministic policies without losing computational tractability and (2) in the ability to enforce the provable satisfaction of arbitrarily many specifications.
  arXiv  Detail & Related papers  (2020-07-16T14:22:55Z)
• Variational Policy Propagation for Multi-agent Reinforcement Learning [68.26579560607597]
  We propose a emphcollaborative multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a emphjoint policy through the interactions over agents. We prove that the joint policy is a Markov Random Field under some mild conditions, which in turn reduces the policy space effectively. We integrate the variational inference as special differentiable layers in policy such as the actions can be efficiently sampled from the Markov Random Field and the overall policy is differentiable.
  arXiv  Detail & Related papers  (2020-04-19T15:42:55Z)
• Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes [36.07746952116073]
  We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP) We show how to use point-based value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula. We demonstrate that our method scales to large POMDP domains and provides strong bounds on the performance of the resulting policy.
  arXiv  Detail & Related papers  (2020-01-11T23:09:25Z)

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.