Cluster-based Sampling in Hindsight Experience Replay for Robotic Tasks (Student Abstract)

• URL: http://arxiv.org/abs/2208.14741v4
• Date: Wed, 10 Jan 2024 07:48:06 GMT
• Title: Cluster-based Sampling in Hindsight Experience Replay for Robotic Tasks (Student Abstract)
• Authors: Taeyoung Kim, Dongsoo Har
• Abstract summary: This paper investigates the impact of exploiting the property of achieved goals in generating successful experiences. The proposed sampling strategy groups episodes with different achieved goals by using a cluster model and samples experiences in the manner of HER. The results of experiments demonstrate that the proposed method is substantially sample efficient and achieves better performance than baseline approaches.
• Score: 3.4616343332323596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-goal reinforcement learning with a sparse binary reward, training agents is particularly challenging, due to a lack of successful experiences. To solve this problem, hindsight experience replay (HER) generates successful experiences even from unsuccessful ones. However, generating successful experiences from uniformly sampled ones is not an efficient process. In this paper, the impact of exploiting the property of achieved goals in generating successful experiences is investigated and a novel cluster-based sampling strategy is proposed. The proposed sampling strategy groups episodes with different achieved goals by using a cluster model and samples experiences in the manner of HER to create the training batch. The proposed method is validated by experiments with three robotic control tasks of the OpenAI Gym. The results of experiments demonstrate that the proposed method is substantially sample efficient and achieves better performance than baseline approaches.

Related papers

• Efficient Diversity-based Experience Replay for Deep Reinforcement Learning [14.96744975805832]
  This paper proposes a novel approach, diversity-based experience replay (DBER), which leverages the deterministic point process to prioritize diverse samples in state realizations. We conducted extensive experiments on Robotic Manipulation tasks in MuJoCo, Atari games, and realistic in-door environments in Habitat.
  arXiv  Detail & Related papers  (2024-10-27T15:51:27Z)
• Adaptive teachers for amortized samplers [76.88721198565861]
  Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is intractable. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose an adaptive training distribution (the Teacher) to guide the training of the primary amortized sampler (the Student) by prioritizing high-loss regions.
  arXiv  Detail & Related papers  (2024-10-02T11:33:13Z)
• Random Latent Exploration for Deep Reinforcement Learning [71.88709402926415]
  This paper introduces a new exploration technique called Random Latent Exploration (RLE) RLE combines the strengths of bonus-based and noise-based (two popular approaches for effective exploration in deep RL) exploration strategies. We evaluate it on the challenging Atari and IsaacGym benchmarks and show that RLE exhibits higher overall scores across all the tasks than other approaches.
  arXiv  Detail & Related papers  (2024-07-18T17:55:22Z)
• SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning [22.389803019100423]
  We propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay. Our method achieves the best performance on almost all maps compared to other popular algorithms in a set of StarCraft II micromanagement games.
  arXiv  Detail & Related papers  (2023-03-16T03:17:20Z)
• Sampling Through the Lens of Sequential Decision Making [9.101505546901999]
  We propose a reward-guided sampling strategy called Adaptive Sample with Reward (ASR) Our approach optimally adjusts the sampling process to achieve optimal performance. Empirical results in information retrieval and clustering demonstrate ASR's superb performance across different datasets.
  arXiv  Detail & Related papers  (2022-08-17T04:01:29Z)
• SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning [168.89470249446023]
  We present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. Our experiments demonstrate that our approach significantly improves the feedback-efficiency of the preference-based method on a variety of locomotion and robotic manipulation tasks.
  arXiv  Detail & Related papers  (2022-03-18T16:50:38Z)
• An Investigation of Replay-based Approaches for Continual Learning [79.0660895390689]
  Continual learning (CL) is a major challenge of machine learning (ML) and describes the ability to learn several tasks sequentially without catastrophic forgetting (CF) Several solution classes have been proposed, of which so-called replay-based approaches seem very promising due to their simplicity and robustness. We empirically investigate replay-based approaches of continual learning and assess their potential for applications.
  arXiv  Detail & Related papers  (2021-08-15T15:05:02Z)
• MHER: Model-based Hindsight Experience Replay [33.00149668905828]
  We propose Model-based Hindsight Experience Replay (MHER) to solve multi-goal reinforcement learning problems. replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method. MHER exploits experiences more efficiently by leveraging environmental dynamics to generate virtual achieved goals.
  arXiv  Detail & Related papers  (2021-07-01T08:52:45Z)
• Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations [78.94386823185724]
  Imitation learning learns effectively in sparse-rewarded tasks by leveraging the existing expert demonstrations. In practice, collecting a sufficient amount of expert demonstrations can be prohibitively expensive. We propose Self-Adaptive Learning (SAIL) that can achieve (near) optimal performance given only a limited number of sub-optimal demonstrations.
  arXiv  Detail & Related papers  (2020-04-01T15:57:15Z)
• Soft Hindsight Experience Replay [77.99182201815763]
  Soft Hindsight Experience Replay (SHER) is a novel approach based on HER and Maximum Entropy Reinforcement Learning (MERL) We evaluate SHER on Open AI Robotic manipulation tasks with sparse rewards.
  arXiv  Detail & Related papers  (2020-02-06T03:57:04Z)

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.