Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning

• URL: http://arxiv.org/abs/2408.09125v1
• Date: Sat, 17 Aug 2024 07:17:19 GMT
• Title: Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning
• Authors: Rishabh Agrawal, Nathan Dahlin, Rahul Jain, Ashutosh Nayyar,
• Abstract summary: We address a scenario where the imitator relies solely on observed behavior and cannot make environmental interactions during learning. Unlike state-of-the-art (SOTA IL) methods, this approach tackles the limitations of conventional IL by operating in a more constrained and realistic setting. We demonstrate consistently superior empirical performance compared to many SOTA IL algorithms.
• Score: 8.92571113137362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation learning (IL) is notably effective for robotic tasks where directly programming behaviors or defining optimal control costs is challenging. In this work, we address a scenario where the imitator relies solely on observed behavior and cannot make environmental interactions during learning. It does not have additional supplementary datasets beyond the expert's dataset nor any information about the transition dynamics. Unlike state-of-the-art (SOTA) IL methods, this approach tackles the limitations of conventional IL by operating in a more constrained and realistic setting. Our method uses the Markov balance equation and introduces a novel conditional density estimation-based imitation learning framework. It employs conditional normalizing flows for transition dynamics estimation and aims at satisfying a balance equation for the environment. Through a series of numerical experiments on Classic Control and MuJoCo environments, we demonstrate consistently superior empirical performance compared to many SOTA IL algorithms.

Related papers

• COSBO: Conservative Offline Simulation-Based Policy Optimization [7.696359453385686]
  offline reinforcement learning allows training reinforcement learning models on data from live deployments. In contrast, simulation environments attempting to replicate the live environment can be used instead of the live data. We propose a method that combines an imperfect simulation environment with data from the target environment, to train an offline reinforcement learning policy.
  arXiv  Detail & Related papers  (2024-09-22T12:20:55Z)
• Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness [30.44174123736964]
  We introduce an innovative adaptive fairness-aware online meta-learning algorithm, referred to as FairSAOML. Our experimental evaluation on various real-world datasets in dynamic environments demonstrates that our proposed FairSAOML algorithm consistently outperforms alternative approaches.
  arXiv  Detail & Related papers  (2024-02-19T17:44:35Z)
• Conditional Kernel Imitation Learning for Continuous State Environments [9.750698192309978]
  We introduce a novel conditional kernel density estimation-based imitation learning framework. We show consistently superior empirical performance over many state-of-the-art IL algorithms.
  arXiv  Detail & Related papers  (2023-08-24T05:26:42Z)
• Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics [96.9177297872723]
  We present a novel method for guaranteeing linear momentum in learned physics simulations. We enforce conservation of momentum with a hard constraint, which we realize via antisymmetrical continuous convolutional layers. In combination, the proposed method allows us to increase the physical accuracy of the learned simulator substantially.
  arXiv  Detail & Related papers  (2022-10-12T09:12:59Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Robust Learning from Observation with Model Misspecification [33.92371002674386]
  Imitation learning (IL) is a popular paradigm for training policies in robotic systems. We propose a robust IL algorithm to learn policies that can effectively transfer to the real environment without fine-tuning.
  arXiv  Detail & Related papers  (2022-02-12T07:04:06Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)
• Strictly Batch Imitation Learning by Energy-based Distribution Matching [104.33286163090179]
  Consider learning a policy purely on the basis of demonstrated behavior -- that is, with no access to reinforcement signals, no knowledge of transition dynamics, and no further interaction with the environment. One solution is simply to retrofit existing algorithms for apprenticeship learning to work in the offline setting. But such an approach leans heavily on off-policy evaluation or offline model estimation, and can be indirect and inefficient. We argue that a good solution should be able to explicitly parameterize a policy, implicitly learn from rollout dynamics, and operate in an entirely offline fashion.
  arXiv  Detail & Related papers  (2020-06-25T03:27:59Z)

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.