Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning

Li, Ying; Xu, De

doi:10.1007/s11633-021-1290-3

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…Due to the high cost of time and space, basic CFR is not applicable to HUNL, which is much more complex than limited poker. Various improved CFR approaches have been developed, considering improving computing speed or compressing the required storage space [31,32] . For example, based on CFR, continue-resolving [18] , and safe and nested subgame solving [8] , are key factors for success of the DeepStack and Libratus, respectively.…”

Section: Cfr For Deepstack and Libratusmentioning

confidence: 99%

AI in Human-computer Gaming: Techniques, Challenges and Opportunities

et al. 2023

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With the breakthrough of AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all over the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed, such as Libratus, OpenAI Five, and AlphaStar, which beat professional human players. The rapid development of human-computer gaming AIs indicates a big step for decision-making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question arises: What are the possible challenges of current techniques in human-computer gaming and what are the future trends? To answer the above question, in this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs, and real-time strategy game AIs. Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human-level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer games; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs. Finally, we hope that this brief review can provide an introduction for beginners and inspire insight for researchers in the field of AI in human-computer gaming.

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Section: Cfr For Deepstack and Libratusmentioning

confidence: 99%

AI in Human-computer Gaming: Techniques, Challenges and Opportunities

et al. 2023

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Weakly Correlated Knowledge Integration for Few-shot Image Classification

2022

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Various few-shot image classification methods indicate that transferring knowledge from other sources can improve the accuracy of the classification. However, most of these methods work with one single source or use only closely correlated knowledge sources. In this paper, we propose a novel weakly correlated knowledge integration (WCKI) framework to address these issues. More specifically, we propose a unified knowledge graph (UKG) to integrate knowledge transferred from different sources (i.e., visual domain and textual domain). Moreover, a graph attention module is proposed to sample the subgraph from the UKG with low complexity. To avoid explicitly aligning the visual features to the potentially biased and weakly correlated knowledge space, we sample a task-specific subgraph from UKG and append it as latent variables. Our framework demonstrates significant improvements on multiple few-shot image classification datasets.

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Offline Pre-trained Multi-agent Decision Transformer

Meng

Wen

et al. 2023

Mach. Intell. Res.

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Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the combinatorially increased interactions among agents and with the environment. However, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor even datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets and using them to examine the usage of the decision transformer in the context of MARL. We investigate the generalization of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to online fine tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages the transformer’s modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A significant benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios. On the StarCraft II offline dataset, MADT outperforms the state-of-the-art offline reinforcement learning (RL) baselines, including BCQ and CQL. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency and enjoys strong performance in both few-short and zero-shot cases. To the best of our knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalizability enhancements for MARL.

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Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning

Cited by 8 publications

References 20 publications

AI in Human-computer Gaming: Techniques, Challenges and Opportunities

AI in Human-computer Gaming: Techniques, Challenges and Opportunities

Weakly Correlated Knowledge Integration for Few-shot Image Classification

Offline Pre-trained Multi-agent Decision Transformer

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