A Method for Fast Autonomy Transfer in Reinforcement Learning

• URL: http://arxiv.org/abs/2407.20466v1
• Date: Mon, 29 Jul 2024 23:48:07 GMT
• Title: A Method for Fast Autonomy Transfer in Reinforcement Learning
• Authors: Dinuka Sahabandu, Bhaskar Ramasubramanian, Michail Alexiou, J. Sukarno Mertoguno, Linda Bushnell, Radha Poovendran,
• Abstract summary: This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer. Unlike traditional methods that require extensive retraining or fine-tuning, our approach integrates existing knowledge, enabling an RL agent to adapt swiftly to new settings.
• Score: 3.8049020806504967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple environments. Unlike traditional methods that require extensive retraining or fine-tuning, our approach integrates existing knowledge, enabling an RL agent to adapt swiftly to new settings without requiring extensive computational resources. Our contributions include development of the Multi-Critic Actor-Critic (MCAC) algorithm, establishing its convergence, and empirical evidence demonstrating its efficacy. Our experimental results show that MCAC significantly outperforms the baseline actor-critic algorithm, achieving up to 22.76x faster autonomy transfer and higher reward accumulation. This advancement underscores the potential of leveraging accumulated knowledge for efficient adaptation in RL applications.

Related papers

• Online inductive learning from answer sets for efficient reinforcement learning exploration [52.03682298194168]
  We exploit inductive learning of answer set programs to learn a set of logical rules representing an explainable approximation of the agent policy. We then perform answer set reasoning on the learned rules to guide the exploration of the learning agent at the next batch. Our methodology produces a significant boost in the discounted return achieved by the agent, even in the first batches of training.
  arXiv  Detail & Related papers  (2025-01-13T16:13:22Z)
• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach [0.9549646359252346]
  In deep Reinforcement Learning (RL) models trained using gradient-based techniques, the choice of gradient and its learning rate are crucial to achieving good performance. We propose dynamic Learning Rate for deep Reinforcement Learning (LRRL), a meta-learning approach that selects the learning rate based on the agent's performance during training.
  arXiv  Detail & Related papers  (2024-10-16T14:15:28Z)
• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach [11.11570399751075]
  This research presents a novel framework that harnesses the potential of Deep Reinforcement Learning (DRL) The integration of our DRL agent with the RAY platform facilitates the creation of RLlib-IMPALA, a novel framework that efficiently uses RAY's resources to improve system adaptability and control.
  arXiv  Detail & Related papers  (2024-02-24T23:25:35Z)
• An advantage based policy transfer algorithm for reinforcement learning with measures of transferability [5.926203312586109]
  Reinforcement learning (RL) enables sequential decision-making in complex and high-dimensional environments. This paper proposes an off-policy Advantage-based Policy Transfer algorithm, APT-RL, for fixed domain environments.
  arXiv  Detail & Related papers  (2023-11-12T04:25:53Z)
• Learning to Optimize for Reinforcement Learning [58.01132862590378]
  Reinforcement learning (RL) is essentially different from supervised learning, and in practice, these learneds do not work well even in simple RL tasks. Agent-gradient distribution is non-independent and identically distributed, leading to inefficient meta-training. We show that, although only trained in toy tasks, our learned can generalize unseen complex tasks in Brax.
  arXiv  Detail & Related papers  (2023-02-03T00:11:02Z)
• Lean Evolutionary Reinforcement Learning by Multitasking with Importance Sampling [20.9680985132322]
  We introduce a novel neuroevolutionary multitasking (NuEMT) algorithm to transfer information from a set of auxiliary tasks to the target (full length) RL task. We demonstrate that the NuEMT algorithm data-lean evolutionary RL, reducing expensive agent-environment interaction data requirements.
  arXiv  Detail & Related papers  (2022-03-21T10:06:16Z)
• Persistent Reinforcement Learning via Subgoal Curricula [114.83989499740193]
  Value-accelerated Persistent Reinforcement Learning (VaPRL) generates a curriculum of initial states. VaPRL reduces the interventions required by three orders of magnitude compared to episodic reinforcement learning.
  arXiv  Detail & Related papers  (2021-07-27T16:39:45Z)
• Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning [114.07623388322048]
  We discuss how the standard goal-conditioned RL (GCRL) is encapsulated by the objective variational empowerment. Our work lays a novel foundation from which to evaluate, analyze, and develop representation learning techniques in goal-based RL.
  arXiv  Detail & Related papers  (2021-06-02T18:12:26Z)
• KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning [16.167201058368303]
  Deep Reinforcement Learning (RL) algorithms have achieved dramatically progress in the multi-agent area. To alleviate this problem, efficient leveraging of the historical experience is essential. We propose a method, named "KnowRU" for knowledge reusing.
  arXiv  Detail & Related papers  (2021-03-27T12:38:01Z)

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.