Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls

• URL: http://arxiv.org/abs/2405.12355v1
• Date: Mon, 20 May 2024 20:06:54 GMT
• Title: Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls
• Authors: Nathaniel Hamilton, Kyle Dunlap, Kerianne L. Hobbs,
• Abstract summary: This paper analyzes using discrete action spaces, where the agent must choose from a predefined list of actions. Experiments are conducted for an inspection task, where the agent must circumnavigate an object to inspect points on its surface, and a docking task, where the agent must move into proximity of another spacecraft and "dock" A common objective of both tasks, and most space tasks in general, is to minimize fuel usage, which motivates the agent to regularly choose an action that uses no fuel.
• Score: 0.3441021278275805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For many space applications, traditional control methods are often used during operation. However, as the number of space assets continues to grow, autonomous operation can enable rapid development of control methods for different space related tasks. One method of developing autonomous control is Reinforcement Learning (RL), which has become increasingly popular after demonstrating promising performance and success across many complex tasks. While it is common for RL agents to learn bounded continuous control values, this may not be realistic or practical for many space tasks that traditionally prefer an on/off approach for control. This paper analyzes using discrete action spaces, where the agent must choose from a predefined list of actions. The experiments explore how the number of choices provided to the agents affects their measured performance during and after training. This analysis is conducted for an inspection task, where the agent must circumnavigate an object to inspect points on its surface, and a docking task, where the agent must move into proximity of another spacecraft and "dock" with a low relative speed. A common objective of both tasks, and most space tasks in general, is to minimize fuel usage, which motivates the agent to regularly choose an action that uses no fuel. Our results show that a limited number of discrete choices leads to optimal performance for the inspection task, while continuous control leads to optimal performance for the docking task.

Related papers

• Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces [52.649077293256795]
  Continual offline reinforcement learning (CORL) has shown impressive ability in diffusion-based lifelong learning systems. We propose Vector-Quantized Continual diffuser, named VQ-CD, to break the barrier of different spaces between various tasks.
  arXiv  Detail & Related papers  (2024-10-21T07:13:45Z)
• Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
  In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency. In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution. Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
  arXiv  Detail & Related papers  (2024-04-05T17:58:37Z)
• Latent Exploration for Reinforcement Learning [87.42776741119653]
  In Reinforcement Learning, agents learn policies by exploring and interacting with the environment. We propose LATent TIme-Correlated Exploration (Lattice), a method to inject temporally-correlated noise into the latent state of the policy network.
  arXiv  Detail & Related papers  (2023-05-31T17:40:43Z)
• CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces [9.578169216444813]
  This paper proposes an approach to coordinating multi-robot manipulation through learned latent action spaces that are shared across different agents. We validate our method in simulated multi-robot manipulation tasks and demonstrate improvement over previous baselines in terms of sample efficiency and learning performance.
  arXiv  Detail & Related papers  (2022-11-28T23:20:47Z)
• Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning [1.212848031108815]
  We propose an end-to-end active visual tracking method based on DQN algorithm, named as DRLAVT. It can guide the chasing spacecraft approach to arbitrary space non-cooperative target merely relied on color or RGBD images. It significantly outperforms position-based visual servoing baseline algorithm that adopts state-of-the-art 2D monocular tracker, SiamRPN.
  arXiv  Detail & Related papers  (2021-12-18T06:12:24Z)
• Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies [45.20170713261535]
  We investigate the phenomenon that trained agents often prefer actions at the boundaries of that space. We replace the normal Gaussian by a Bernoulli distribution that solely considers the extremes along each action dimension. Surprisingly, this achieves state-of-the-art performance on several continuous control benchmarks.
  arXiv  Detail & Related papers  (2021-11-03T22:45:55Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)
• Planning to Explore via Self-Supervised World Models [120.31359262226758]
  Plan2Explore is a self-supervised reinforcement learning agent. We present a new approach to self-supervised exploration and fast adaptation to new tasks. Without any training supervision or task-specific interaction, Plan2Explore outperforms prior self-supervised exploration methods.
  arXiv  Detail & Related papers  (2020-05-12T17:59:45Z)
• Weakly-Supervised Reinforcement Learning for Controllable Behavior [126.04932929741538]
  Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. In many settings, an agent must winnow down the inconceivably large space of all possible tasks to the single task that it is currently being asked to solve. We introduce a framework for using weak supervision to automatically disentangle this semantically meaningful subspace of tasks from the enormous space of nonsensical "chaff" tasks.
  arXiv  Detail & Related papers  (2020-04-06T17:50:28Z)

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.