Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey

• URL: http://arxiv.org/abs/2012.09830v2
• Date: Tue, 16 Mar 2021 14:50:22 GMT
• Title: Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
• Authors: C\'edric Colas, Tristan Karch, Olivier Sigaud, Pierre-Yves Oudeyer
• Abstract summary: Developmental approaches argue that learning agents must generate, select and learn to solve their own problems. Recent years have seen a convergence of developmental approaches and deep reinforcement learning (RL) methods, forming the new domain of developmental machine learning. This paper proposes a typology of these methods at the intersection of deep RL and developmental approaches, surveys recent approaches and discusses future avenues.
• Score: 21.311739361361717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building autonomous machines that can explore open-ended environments, discover possible interactions and autonomously build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by autonomous and intrinsically motivated learning agents that can generate, select and learn to solve their own problems. In recent years, we have seen a convergence of developmental approaches, and developmental robotics in particular, with deep reinforcement learning (RL) methods, forming the new domain of developmental machine learning. Within this new domain, we review here a set of methods where deep RL algorithms are trained to tackle the developmental robotics problem of the autonomous acquisition of open-ended repertoires of skills. Intrinsically motivated goal-conditioned RL algorithms train agents to learn to represent, generate and pursue their own goals. The self-generation of goals requires the learning of compact goal encodings as well as their associated goal-achievement functions, which results in new challenges compared to traditional RL algorithms designed to tackle pre-defined sets of goals using external reward signals. This paper proposes a typology of these methods at the intersection of deep RL and developmental approaches, surveys recent approaches and discusses future avenues.

Related papers

• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [85.21378553454672]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges [2.9008806248012333]
  Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. We will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area.
  arXiv  Detail & Related papers  (2023-07-31T18:00:02Z)
• Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data [101.43350024175157]
  Self-supervised learning has the potential to decrease the amount of human annotation and engineering effort required to learn control strategies. Our work builds on prior work showing that the reinforcement learning (RL) itself can be cast as a self-supervised problem. We demonstrate that a self-supervised RL algorithm based on contrastive learning can solve real-world, image-based robotic manipulation tasks.
  arXiv  Detail & Related papers  (2023-06-06T01:36:56Z)
• Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning [99.38163119531745]
  We show that applying a discretizing bottleneck can improve performance in goal-conditioned RL setups. We experimentally prove the expected return on out-of-distribution goals, while still allowing for specifying goals with expressive structure.
  arXiv  Detail & Related papers  (2022-11-01T03:31:43Z)
• Deep Reinforcement Learning for Multi-Agent Interaction [14.532965827043254]
  The Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.
  arXiv  Detail & Related papers  (2022-08-02T21:55:56Z)
• Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies [64.0476282000118]
  Intrinsic motivations have proven to generate a task-agnostic signal to properly allocate the training time amongst goals. While the majority of works in the field of intrinsically motivated open-ended learning focus on scenarios where goals are independent from each other, only few of them studied the autonomous acquisition of interdependent tasks. In particular, we first deepen the analysis of a previous system, showing the importance of incorporating information about the relationships between tasks at a higher level of the architecture. Then we introduce H-GRAIL, a new system that extends the previous one by adding a new learning layer to store the autonomously acquired sequences
  arXiv  Detail & Related papers  (2022-05-16T10:43:01Z)
• Learning with AMIGo: Adversarially Motivated Intrinsic Goals [63.680207855344875]
  AMIGo is a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals. We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks.
  arXiv  Detail & Related papers  (2020-06-22T10:22:08Z)
• Automatic Curriculum Learning through Value Disagreement [95.19299356298876]
  Continually solving new, unsolved tasks is the key to learning diverse behaviors. In the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency. We propose setting up an automatic curriculum for goals that the agent needs to solve. We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-17T03:58:25Z)

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.