Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems

• URL: http://arxiv.org/abs/2202.01534v1
• Date: Thu, 3 Feb 2022 11:33:58 GMT
• Title: Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems
• Authors: Miguel Suau, Jinke He, Matthijs T. J. Spaan, Frans A. Oliehoek
• Abstract summary: In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for deep RL. We focus on domains where agents interact with a reduced portion of a larger environment while still being affected by the global dynamics. Our method combines the use of local simulators with learned models that mimic the influence of the global system.
• Score: 18.281902746944525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for deep RL to be applicable. We focus on domains where agents interact with a reduced portion of a larger environment while still being affected by the global dynamics. Our method combines the use of local simulators with learned models that mimic the influence of the global system. The experiments reveal that incorporating this idea into the deep RL workflow can considerably accelerate the training process and presents several opportunities for the future.

Related papers

• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning [7.714620721734689]
  "Box o Flows" is an experimental control system for systematically evaluating RL algorithms in dynamic real-world scenarios. We show how state-of-the-art model-free RL algorithms can synthesize a variety of complex behaviors via simple reward specifications. We believe that the insights gained from this preliminary study and the availability of systems like the Box o Flows support the way forward for developing systematic RL algorithms.
  arXiv  Detail & Related papers  (2024-02-08T23:35:03Z)
• Learning fast changing slow in spiking neural networks [3.069335774032178]
  Reinforcement learning (RL) faces substantial challenges when applied to real-life problems. Life-long learning machines must resolve the plasticity-stability paradox. Striking a balance between acquiring new knowledge and maintaining stability is crucial for artificial agents.
  arXiv  Detail & Related papers  (2024-01-25T12:03:10Z)
• REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation [61.7171775202833]
  We introduce an efficient system for learning dexterous manipulation skills withReinforcement learning. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. Our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy.
  arXiv  Detail & Related papers  (2023-09-06T19:05:31Z)
• Actively Learning Costly Reward Functions for Reinforcement Learning [56.34005280792013]
  We show that it is possible to train agents in complex real-world environments orders of magnitudes faster. By enabling the application of reinforcement learning methods to new domains, we show that we can find interesting and non-trivial solutions.
  arXiv  Detail & Related papers  (2022-11-23T19:17:20Z)
• When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning [7.786094194874359]
  We propose the Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning (H2O) framework to provide an affirmative answer to this question. H2O introduces a dynamics-aware policy evaluation scheme, which adaptively penalizes the Q function learning on simulated state-action pairs with large dynamics gaps. We demonstrate the superior performance of H2O against other cross-domain online and offline RL algorithms.
  arXiv  Detail & Related papers  (2022-06-27T17:18:11Z)
• Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems [17.10464381844892]
  Reinforcement learning (RL) is highly suitable for devising control strategies in the context of dynamical systems. Recent research results indicate that RL-augmented computational fluid dynamics (CFD) solvers can exceed the current state of the art. We present Relexi as a scalable RL framework that bridges the gap between machine learning and modern CFD solvers on HPC systems.
  arXiv  Detail & Related papers  (2022-05-13T08:21:18Z)
• Accelerated Policy Learning with Parallel Differentiable Simulation [59.665651562534755]
  We present a differentiable simulator and a new policy learning algorithm (SHAC) Our algorithm alleviates problems with local minima through a smooth critic function. We show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms.
  arXiv  Detail & Related papers  (2022-04-14T17:46:26Z)
• Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning [0.8399688944263843]
  Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. Sim-to-real approaches leverage simulations to pretrain DRL policies and then deploy them in the real world. This work proposes a distributed cloud-edge architecture to train DRL agents in the real world in real-time.
  arXiv  Detail & Related papers  (2022-03-04T10:27:01Z)
• AWAC: Accelerating Online Reinforcement Learning with Offline Datasets [84.94748183816547]
  We show that our method, advantage weighted actor critic (AWAC), enables rapid learning of skills with a combination of prior demonstration data and online experience. Our results show that incorporating prior data can reduce the time required to learn a range of robotic skills to practical time-scales.
  arXiv  Detail & Related papers  (2020-06-16T17:54:41Z)
• Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks [70.56451186797436]
  We study how to use meta-reinforcement learning to solve the bulk of the problem in simulation. We demonstrate our approach by training an agent to successfully perform challenging real-world insertion tasks.
  arXiv  Detail & Related papers  (2020-04-29T18:00:22Z)

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.