Curriculum Learning with Hindsight Experience Replay for Sequential
Object Manipulation Tasks
- URL: http://arxiv.org/abs/2008.09377v1
- Date: Fri, 21 Aug 2020 08:59:28 GMT
- Title: Curriculum Learning with Hindsight Experience Replay for Sequential
Object Manipulation Tasks
- Authors: Binyamin Manela, Armin Biess
- Abstract summary: We present an algorithm that combines curriculum learning with Hindsight Experience Replay (HER) to learn sequential object manipulation tasks.
The algorithm exploits the recurrent structure inherent in many object manipulation tasks and implements the entire learning process in the original simulation without adjusting it to each source task.
- Score: 1.370633147306388
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Learning complex tasks from scratch is challenging and often impossible for
humans as well as for artificial agents. A curriculum can be used instead,
which decomposes a complex task (target task) into a sequence of source tasks
(the curriculum). Each source task is a simplified version of the next source
task with increasing complexity. Learning then occurs gradually by training on
each source task while using knowledge from the curriculum's prior source
tasks. In this study, we present a new algorithm that combines curriculum
learning with Hindsight Experience Replay (HER), to learn sequential object
manipulation tasks for multiple goals and sparse feedback. The algorithm
exploits the recurrent structure inherent in many object manipulation tasks and
implements the entire learning process in the original simulation without
adjusting it to each source task. We have tested our algorithm on three
challenging throwing tasks and show vast improvements compared to vanilla-HER.
Related papers
- Multitask Learning with No Regret: from Improved Confidence Bounds to
Active Learning [79.07658065326592]
Quantifying uncertainty in the estimated tasks is of pivotal importance for many downstream applications, such as online or active learning.
We provide novel multitask confidence intervals in the challenging setting when neither the similarity between tasks nor the tasks' features are available to the learner.
We propose a novel online learning algorithm that achieves such improved regret without knowing this parameter in advance.
arXiv Detail & Related papers (2023-08-03T13:08:09Z) - Reinforcement Learning with Success Induced Task Prioritization [68.8204255655161]
We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning.
The algorithm selects the order of tasks that provide the fastest learning for agents.
We demonstrate that SITP matches or surpasses the results of other curriculum design methods.
arXiv Detail & Related papers (2022-12-30T12:32:43Z) - Teacher-student curriculum learning for reinforcement learning [1.7259824817932292]
Reinforcement learning (rl) is a popular paradigm for sequential decision making problems.
The sample inefficiency of deep reinforcement learning methods is a significant obstacle when applying rl to real-world problems.
We propose a teacher-student curriculum learning setting where we simultaneously train a teacher that selects tasks for the student while the student learns how to solve the selected task.
arXiv Detail & Related papers (2022-10-31T14:45:39Z) - Fast Inference and Transfer of Compositional Task Structures for
Few-shot Task Generalization [101.72755769194677]
We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph.
Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks.
Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
arXiv Detail & Related papers (2022-05-25T10:44:25Z) - Active Multi-Task Representation Learning [50.13453053304159]
We give the first formal study on resource task sampling by leveraging the techniques from active learning.
We propose an algorithm that iteratively estimates the relevance of each source task to the target task and samples from each source task based on the estimated relevance.
arXiv Detail & Related papers (2022-02-02T08:23:24Z) - Efficiently Identifying Task Groupings for Multi-Task Learning [55.80489920205404]
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
We suggest an approach to select which tasks should train together in multi-task learning models.
Our method determines task groupings in a single training run by co-training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss.
arXiv Detail & Related papers (2021-09-10T02:01:43Z) - Multi-task curriculum learning in a complex, visual, hard-exploration
domain: Minecraft [18.845438529816004]
We explore curriculum learning in a complex, visual domain with many hard exploration challenges: Minecraft.
We find that learning progress is a reliable measure of learnability for automatically constructing an effective curriculum.
arXiv Detail & Related papers (2021-06-28T17:50:40Z) - Reset-Free Reinforcement Learning via Multi-Task Learning: Learning
Dexterous Manipulation Behaviors without Human Intervention [67.1936055742498]
We show that multi-task learning can effectively scale reset-free learning schemes to much more complex problems.
This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.
arXiv Detail & Related papers (2021-04-22T17:38:27Z) - Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer
Learning to Discover Task Hierarchy [0.0]
In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning.
We show that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task.
We propose a task-oriented representation of complex actions, called procedures, to learn online task relationships and unbounded sequences of action primitives to control the different observables of the environment.
arXiv Detail & Related papers (2021-02-19T10:44:08Z) - Representation Ensembling for Synergistic Lifelong Learning with
Quasilinear Complexity [17.858926093389737]
In lifelong learning, data are used to improve performance not only on the current task, but also on previously encountered, and as yet unencountered tasks.
Our key insight is that we can synergistically ensemble representations -- that were learned independently on disparate tasks -- to enable both forward and backward transfer.
arXiv Detail & Related papers (2020-04-27T16:16:30Z)
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.