Curriculum Learning with Hindsight Experience Replay for Sequential Object Manipulation Tasks

@article{Manela2022CurriculumLW,
  title={Curriculum Learning with Hindsight Experience Replay for Sequential Object Manipulation Tasks},
  author={Binyamin Manela and Armin Biess},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2022},
  volume={145},
  pages={
          260-270
        }
}
  • Binyamin ManelaA. Biess
  • Published 21 August 2020
  • Computer Science
  • Neural networks : the official journal of the International Neural Network Society

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References

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This paper forms the design of a curriculum as a Markov Decision Process, which directly models the accumulation of knowledge as an agent interacts with tasks, and proposes a method that approximates an execution of an optimal policy in this MDP to produce an agent-specific curriculum.

Hindsight Experience Replay

A novel technique is presented which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering and may be seen as a form of implicit curriculum.

Reverse Curriculum Generation for Reinforcement Learning

This work proposes a method to learn goal-oriented tasks without requiring any prior knowledge other than obtaining a single state in which the task is achieved, and generates a curriculum of start states that adapts to the agent's performance, leading to efficient training on goal- oriented tasks.

Learning Curriculum Policies for Reinforcement Learning

The method is extended to handle multiple transfer learning algorithms, and it is shown for the first time that a curriculum policy over this MDP can be learned from experience.

Source Task Creation for Curriculum Learning

This paper presents the more ambitious problem of curriculum learning in reinforcement learning, in which the goal is to design a sequence of source tasks for an agent to train on, such that final performance or learning speed is improved.

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

This article presents a framework for curriculum learning (CL) in reinforcement learning, and uses it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.

Curriculum learning

It is hypothesized that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions).

Curriculum-guided Hindsight Experience Replay

This paper proposes to adaptively select the failed experiences for replay according to the proximity to the true goals and the curiosity of exploration over diverse pseudo goals, and adopts a human-like learning strategy that enforces more curiosity in earlier stages and changes to larger goal-proximity later.

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This work uses a generator network to propose tasks for the agent to try to achieve, specified as goal states, and shows that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment.