Experimental Study on The Effect of Multi-step Deep Reinforcement Learning in POMDPs

• URL: http://arxiv.org/abs/2209.04999v2
• Date: Wed, 15 Jan 2025 04:45:12 GMT
• Title: Experimental Study on The Effect of Multi-step Deep Reinforcement Learning in POMDPs
• Authors: Lingheng Meng, Rob Gorbet, Michael Burke, Dana Kulić,
• Abstract summary: This paper considers three popular DRL algorithms, namely Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC) We show that SAC and TD3 typically outperform PPO across a broad range of tasks that can be represented as MDPs. We identify this by observing that the inclusion of multi-step bootstrapping in TD3 and SAC results in improved robustness in POMDP settings.
• Score: 3.7186122930334724
• License:
• Abstract: Deep Reinforcement Learning (DRL) has made tremendous advances in both simulated and real-world robot control tasks in recent years. This is particularly the case for tasks that can be carefully engineered with a full state representation, and which can then be formulated as a Markov Decision Process (MDP). However, applying DRL strategies designed for MDPs to novel robot control tasks can be challenging, because the available observations may be a partial representation of the state, resulting in a Partially Observable Markov Decision Process (POMDP). This paper considers three popular DRL algorithms, namely Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC), invented for MDPs, and studies their performance in POMDP scenarios. While prior work has found that SAC and TD3 typically outperform PPO across a broad range of tasks that can be represented as MDPs, we show that this is not always the case, using three representative POMDP environments. Empirical studies show that this is related to multi-step bootstrapping, where multi-step immediate rewards, instead of one-step immediate reward, are used to calculate the target value estimation of an observation and action pair. We identify this by observing that the inclusion of multi-step bootstrapping in TD3 (MTD3) and SAC (MSAC) results in improved robustness in POMDP settings.

Related papers

• Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes [56.714690083118406]
  In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures has been shown to yield significant benefits to the sample efficiency compared to single-task RL. We investigate whether such a benefit can extend to more general sequential decision making problems, such as partially observable MDPs (POMDPs) and more general predictive state representations (PSRs) We propose a provably efficient algorithm UMT-PSR for finding near-optimal policies for all PSRs, and demonstrate that the advantage of multi-task learning manifests if the joint model class of PSR
  arXiv  Detail & Related papers  (2023-10-20T14:50:28Z)
• The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning [1.4711121887106535]
  Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of scientific progress of the field. In this article, we augment DRL evaluations to consider parameterized families of MDPs. We show that evaluating the MDP family often yields a substantially different relative ranking of methods, casting doubt on what methods should be considered state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-16T18:51:55Z)
• Optimality Guarantees for Particle Belief Approximation of POMDPs [55.83001584645448]
  Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid. We propose a theory characterizing the approximation error of the particle filtering techniques that these algorithms use.
  arXiv  Detail & Related papers  (2022-10-10T21:11:55Z)
• Semi-Markov Offline Reinforcement Learning for Healthcare [57.15307499843254]
  We introduce three offline RL algorithms, namely, SDQN, SDDQN, and SBCQ. We experimentally demonstrate that only these algorithms learn the optimal policy in variable-time environments. We apply our new algorithms to a real-world offline dataset pertaining to warfarin dosing for stroke prevention.
  arXiv  Detail & Related papers  (2022-03-17T14:51:21Z)
• Robust Entropy-regularized Markov Decision Processes [23.719568076996662]
  We study a robust version of the ER-MDP model, where the optimal policies are required to be robust. We show that essential properties that hold for the non-robust ER-MDP and robust unregularized MDP models also hold in our settings. We show how our framework and results can be integrated into different algorithmic schemes including value or (modified) policy.
  arXiv  Detail & Related papers  (2021-12-31T09:50:46Z)
• Safe Exploration by Solving Early Terminated MDP [77.10563395197045]
  We introduce a new approach to address safe RL problems under the framework of Early TerminatedP (ET-MDP) We first define the ET-MDP as an unconstrained algorithm with the same optimal value function as its corresponding CMDP. An off-policy algorithm based on context models is then proposed to solve the ET-MDP, which thereby solves the corresponding CMDP with better performance and improved learning efficiency.
  arXiv  Detail & Related papers  (2021-07-09T04:24:40Z)
• Memory-based Deep Reinforcement Learning for POMDP [7.137228786549488]
  Long-Short-Term-Memory-based Twin Delayed Deep Deterministic Policy Gradient (LSTM-TD3) Our results demonstrate the significant advantages of the memory component in addressing Partially Observable MDPs.
  arXiv  Detail & Related papers  (2021-02-24T15:25:13Z)
• Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic [59.94347858883343]
  This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) The novelty is to design an embedded product MDP (EP-MDP) between the LDGBA and the MDP. The proposed LDGBA-based reward shaping and discounting schemes for the model-free reinforcement learning (RL) only depend on the EP-MDP states.
  arXiv  Detail & Related papers  (2021-02-24T01:11:25Z)
• Learning Robust State Abstractions for Hidden-Parameter Block MDPs [55.31018404591743]
  We leverage ideas of common structure from the HiP-MDP setting to enable robust state abstractions inspired by Block MDPs. We derive instantiations of this new framework for both multi-task reinforcement learning (MTRL) and meta-reinforcement learning (Meta-RL) settings.
  arXiv  Detail & Related papers  (2020-07-14T17:25:27Z)

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.