Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning

• URL: http://arxiv.org/abs/2002.02697v2
• Date: Mon, 21 Dec 2020 16:16:31 GMT
• Title: Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning
• Authors: Sha Luo, Hamidreza Kasaei, Lambert Schomaker
• Abstract summary: We focus on accelerating reinforcement learning (RL) training and improving the performance of multi-goal reaching tasks. Specifically, we propose a precision-based continuous curriculum learning (PCCL) method in which the requirements are gradually adjusted during the training process. This approach is tested using a Universal Robot 5e in both simulation and real-world multi-goal reach experiments.
• Score: 6.703429330486276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics. However, the required enormous amount of interactive and informative training data provides the major stumbling block for progress. In this study, we focus on accelerating reinforcement learning (RL) training and improving the performance of multi-goal reaching tasks. Specifically, we propose a precision-based continuous curriculum learning (PCCL) method in which the requirements are gradually adjusted during the training process, instead of fixing the parameter in a static schedule. To this end, we explore various continuous curriculum strategies for controlling a training process. This approach is tested using a Universal Robot 5e in both simulation and real-world multi-goal reach experiments. Experimental results support the hypothesis that a static training schedule is suboptimal, and using an appropriate decay function for curriculum learning provides superior results in a faster way.

Related papers

• Reinforcement Learning with Action Sequence for Data-Efficient Robot Learning [62.3886343725955]
  We introduce a novel RL algorithm that learns a critic network that outputs Q-values over a sequence of actions. By explicitly training the value functions to learn the consequence of executing a series of current and future actions, our algorithm allows for learning useful value functions from noisy trajectories.
  arXiv  Detail & Related papers  (2024-11-19T01:23:52Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning [27.73555826776087]
  Reinforcement Learning (RL) allows learning non-trivial robot control laws purely from data. In this paper, we pair a recent algorithm for automatically building curricula with RL on massively parallelized simulations. We demonstrate the potential of curriculum RL to jointly learn state estimation and control for non-linear tracking tasks.
  arXiv  Detail & Related papers  (2023-09-25T12:48:47Z)
• Rethinking Closed-loop Training for Autonomous Driving [82.61418945804544]
  We present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents. We propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines.
  arXiv  Detail & Related papers  (2023-06-27T17:58:39Z)
• Model-Based Reinforcement Learning with Multi-Task Offline Pretraining [59.82457030180094]
  We present a model-based RL method that learns to transfer potentially useful dynamics and action demonstrations from offline data to a novel task. The main idea is to use the world models not only as simulators for behavior learning but also as tools to measure the task relevance. We demonstrate the advantages of our approach compared with the state-of-the-art methods in Meta-World and DeepMind Control Suite.
  arXiv  Detail & Related papers  (2023-06-06T02:24:41Z)
• Automatic Goal Generation using Dynamical Distance Learning [5.797847756967884]
  Reinforcement Learning (RL) agents can learn to solve complex sequential decision making tasks by interacting with the environment. In the field of multi-goal RL, where agents are required to reach multiple goals to solve complex tasks, improving sample efficiency can be especially challenging. We propose a method for automatic goal generation using a dynamical distance function (DDF) in a self-supervised fashion.
  arXiv  Detail & Related papers  (2021-11-07T16:23:56Z)
• Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier [23.886422706697882]
  We propose a novel training procedure named Friendly Training. We show that Friendly Training yields improvements with respect to informed data sub-selection and random selection. Results suggest that adapting the input data is a feasible way to stabilize learning and improve the skills generalization of the network.
  arXiv  Detail & Related papers  (2021-06-21T10:50:34Z)
• Reinforcement Learning for Robust Missile Autopilot Design [0.0]
  This work is pioneer in proposing Reinforcement Learning as a framework for flight control. Under TRPO's methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Results show that it is possible both to achieve the optimal performance and to improve the agent's robustness to uncertainties.
  arXiv  Detail & Related papers  (2020-11-26T09:30:04Z)
• Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning [109.77163932886413]
  We show how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning. This adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning.
  arXiv  Detail & Related papers  (2020-04-21T17:57:04Z)

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.