Attaining Interpretability in Reinforcement Learning via Hierarchical Primitive Composition

• URL: http://arxiv.org/abs/2110.01833v1
• Date: Tue, 5 Oct 2021 05:59:31 GMT
• Title: Attaining Interpretability in Reinforcement Learning via Hierarchical Primitive Composition
• Authors: Jeong-Hoon Lee and Jongeun Choi
• Abstract summary: We propose a novel hierarchical reinforcement learning algorithm that mitigates the aforementioned issues by decomposing the original task in a hierarchy. We show how the proposed scheme can be employed in practice by solving a pick and place task with a 6 DoF manipulator.
• Score: 3.1078562713129765
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep reinforcement learning has shown its effectiveness in various applications and provides a promising direction for solving tasks with high complexity. In most reinforcement learning algorithms, however, two major issues need to be dealt with - the sample inefficiency and the interpretability of a policy. The former happens when the environment is sparsely rewarded and/or has a long-term credit assignment problem, while the latter becomes a problem when the learned policies are deployed at the customer side product. In this paper, we propose a novel hierarchical reinforcement learning algorithm that mitigates the aforementioned issues by decomposing the original task in a hierarchy and by compounding pretrained primitives with intents. We show how the proposed scheme can be employed in practice by solving a pick and place task with a 6 DoF manipulator.

Related papers

• On the benefits of pixel-based hierarchical policies for task generalization [7.207480346660617]
  Reinforcement learning practitioners often avoid hierarchical policies, especially in image-based observation spaces. We analyze the benefits of hierarchy through simulated multi-task robotic control experiments from pixels.
  arXiv  Detail & Related papers  (2024-07-27T01:26:26Z)
• Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition [11.998708550268978]
  We propose an approach which uses an expert-provided decomposition of a task into simpler multi-agent sub-tasks. In each sub-task, a subset of the entire team is trained to acquire sub-task-specific policies. The sub-teams are then merged and transferred to the target task, where their policies are collectively fine-tuned to solve the more complex target task.
  arXiv  Detail & Related papers  (2023-02-09T21:24:56Z)
• Lexicographic Multi-Objective Reinforcement Learning [65.90380946224869]
  We present a family of both action-value and policy gradient algorithms that can be used to solve such problems. We show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-12-28T10:22:36Z)
• Multi-Task Off-Policy Learning from Bandit Feedback [54.96011624223482]
  We propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.
  arXiv  Detail & Related papers  (2022-12-09T08:26:27Z)
• Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control [44.77500987121531]
  Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizon tasks from expert demonstrations. We develop a novel HIL algorithm based on Adversarial Inverse Reinforcement Learning. We also propose a Variational Autoencoder framework for learning with our objectives in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-10-05T00:28:26Z)
• Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization [101.72755769194677]
  We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph. Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:44:25Z)
• Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates [63.58053355357644]
  We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks. We show theoretically that having access to a specific set of diverse policies, which we call a set of independent policies, can allow for instantaneously achieving high-level performance.
  arXiv  Detail & Related papers  (2021-12-30T12:20:46Z)
• Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning [16.12658895065585]
  We argue that representation alone is not enough for efficient transfer in challenging domains and explore how to transfer knowledge through behavior. The behavior of pre-trained policies may be used for solving the task at hand (exploitation) or for collecting useful data to solve the problem (exploration)
  arXiv  Detail & Related papers  (2021-02-24T16:51:02Z)
• Importance Weighted Policy Learning and Adaptation [89.46467771037054]
  We study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning. The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior. Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
  arXiv  Detail & Related papers  (2020-09-10T14:16:58Z)
• Auxiliary Learning by Implicit Differentiation [54.92146615836611]
  Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Here, we propose a novel framework, AuxiLearn, that targets both challenges based on implicit differentiation. First, when useful auxiliaries are known, we propose learning a network that combines all losses into a single coherent objective function. Second, when no useful auxiliary task is known, we describe how to learn a network that generates a meaningful, novel auxiliary task.
  arXiv  Detail & Related papers  (2020-06-22T19:35:07Z)

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.