Skill-Based Meta-Reinforcement Learning
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While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real robot systems infeasible. To mitigate this issue, meta-reinforcement learning methods aim to enable fast learning on novel tasks by learning how to learn. Yet, the application has been limited to short-horizon tasks with dense rewards. To enable learning long-horizon behaviors, recent works have explored leveraging prior experience in the form of offline datasets without reward or task annotations. While these approaches yield improved sample efficiency, millions of interactions with environments are still required to solve complex tasks. In this work, we devise a method that enables meta-learning on long-horizon, sparse-reward tasks, allowing us to solve unseen target tasks with orders of magnitude fewer environment interactions. Our core idea is to leverage prior experience extracted from offline datasets during meta-learning. Specifically, we propose to (1) extract reusable skills and a skill prior from offline datasets, (2) meta-train a high-level policy that learns to efficiently compose learned skills into long-horizon behaviors, and (3) rapidly adapt the meta-trained policy to solve an unseen target task. Experimental results on continuous control tasks in navigation and manipulation demonstrate that the proposed method can efficiently solve long-horizon novel target tasks by combining the strengths of meta-learning and the usage of offline datasets, while prior approaches in RL, meta-RL, and multi-task RL require substantially more environment interactions to solve the tasks.
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Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
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“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.More Related Content
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Task Adaptive Neural Network Search with Meta-Contrastive Learning
Most conventional Neural Architecture Search (NAS) approaches are limited in that they only generate architectures without searching for the optimal parameters. While some NAS methods handle this issue by utilizing a supernet trained on a large-scale dataset such as ImageNet, they may be suboptimal if the target tasks are highly dissimilar from the dataset the supernet is trained on. To address such limitations, we introduce a novel problem of Neural Network Search (NNS), whose goal is to search for the optimal pretrained network for a novel dataset and constraints (e.g. number of parameters), from a model zoo. Then, we propose a novel framework to tackle the problem, namely Task-Adaptive Neural Network Search (TANS). Given a model-zoo that consists of network pretrained on diverse datasets, we use a novel amortized meta-learning framework to learn a cross-modal latent space with contrastive loss, to maximize the similarity between a dataset and a high-performing network on it, and minimize the similarity between irrelevant dataset-network pairs. We validate the effectiveness and efficiency of our method on ten real-world datasets, against existing NAS/AutoML baselines. The results show that our method instantly retrieves networks that outperform models obtained with the baselines with significantly fewer training steps to reach the target performance, thus minimizing the total cost of obtaining a task-optimal network. Our code and the model-zoo are available at https://anonymous.4open.science/r/TANS-33D6
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While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes without any accompanying labels. Such deficiency of labels may result from either high labeling cost, or difficulty of annotation due to the requirement of expert knowledge. Thus the private data at each client may be either partly labeled, or completely unlabeled with labeled data being available only at the server, which leads us to a new practical federated learning problem, namely Federated Semi-Supervised Learning (FSSL). In this work, we study two essential scenarios of FSSL based on the location of the labeled data. The first scenario considers a conventional case where clients have both labeled and unlabeled data (labels-at-client), and the second scenario considers a more challenging case, where the labeled data is only available at the server (labels-at-server). We then propose a novel method to tackle the problems, which we refer to as Federated Matching (FedMatch). FedMatch improves upon naive combinations of federated learning and semi-supervised learning approaches with a new inter-client consistency loss and decomposition of the parameters for disjoint learning on labeled and unlabeled data. Through extensive experimental validation of our method in the two different scenarios, we show that our method outperforms both local semi-supervised learning and baselines which naively combine federated learning with semi-supervised learning.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spirit of unsupervised learning in that the both seek to learn more effective and efficient learning procedure than learning from scratch. The fundamental difference of the two is that the most meta-learning approaches are supervised, assuming full access to the labels. However, acquiring labeled dataset for meta-training not only is costly as it requires human efforts in labeling but also limits its applications to pre-defined task distributions. In this paper, we propose a principled unsupervised meta-learning model, namely Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference. Moreover, we introduce a mixture of Gaussian (GMM) prior, assuming that each modality represents each class-concept in a randomly sampled episode, which we optimize with Expectation-Maximization (EM). Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors. We validate our model on Omniglot and Mini-ImageNet datasets by evaluating its performance on downstream few-shot classification tasks. The results show that our model obtain impressive performance gains over existing unsupervised meta-learning baselines, even outperforming supervised MAML on a certain setting.
Accurate Learning of Graph Representations with Graph Multiset Pooling
Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or average over all node representations considers all node features equally without consideration of their task relevance, and any structural dependencies among them. Recently proposed hierarchical graph pooling methods, on the other hand, may yield the same representation for two different graphs that are distinguished by the Weisfeiler-Lehman test, as they suboptimally preserve information from the node features. To tackle these limitations of existing graph pooling methods, we first formulate the graph pooling problem as a multiset encoding problem with auxiliary information about the graph structure, and propose a Graph Multiset Transformer (GMT) which is a multi-head attention based global pooling layer that captures the interaction between nodes according to their structural dependencies. We show that GMT satisfies both injectiveness and permutation invariance, such that it is at most as powerful as the Weisfeiler-Lehman graph isomorphism test. Moreover, our methods can be easily extended to the previous node clustering approaches for hierarchical graph pooling. Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks with high memory and time efficiency, and obtains even larger performance gain on graph reconstruction and generation tasks.
Contrastive Learning with Adversarial Perturbations for Conditional Text Gene...
Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the “exposure bias” problem. In this work, we propose to mitigate the conditional text generation problem by contrasting positive pairs with negative pairs, such that the model is exposed to various valid or incorrect perturbations of the inputs, for improved generalization. However, training the model with naïve contrastive learning framework using random non-target sequences as negative examples is suboptimal, since they are easily distinguishable from the correct output, especially so with models pretrained with large text corpora. Also, generating positive examples requires domain-specific augmentation heuristics which may not generalize over diverse domains. To tackle this problem, we propose a principled method to generate positive and negative samples for contrastive learning of seq2seq models. Specifically, we generate negative examples by adding small perturbations to the input sequence to minimize its conditional likelihood, and positive examples by adding large perturbations while enforcing it to have a high conditional likelihood. Such “hard” positive and negative pairs generated using our method guides the model to better distinguish correct outputs from incorrect ones. We empirically show that our proposed method significantly improves the generalization of the seq2seq on three text generation tasks — machine translation, text summarization, and question generation.
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Le...
Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tasks, which may be critical (e.g., prediction of mortality risk). Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. However, using loss as a measure of reliability is risky since it could be a result of overfitting. In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality) at a later timestep, but lack of loss at each timestep makes it difficult to measure the reliability at each timestep. To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on feature-level uncertainty. We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms, without any sign of negative transfer. Further qualitative analysis of learned knowledge graphs by clinicians shows that they are helpful in analyzing the predictions of the model. Our final code is available at this https://github.com/anhtuan5696/TPAMTL.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
MetaPerturb is a meta-learned perturbation function that can enhance generalization of neural networks on different tasks and architectures. It proposes a novel meta-learning framework involving jointly training a main model and perturbation module on multiple source tasks to learn a transferable perturbation function. This meta-learned perturbation function can then be transferred to improve performance of a target model on an unseen target task or architecture, outperforming baselines on various datasets and architectures.
Adversarial Self-Supervised Contrastive Learning
Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions, which are then used to augment the training of the model for improved robustness. While some recent works propose semi-supervised adversarial learning methods that utilize unlabeled data, they still require class labels. However, do we really need class labels at all, for adversarially robust training of deep neural networks? In this paper, we propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. Further, we present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data, which aims to maximize the similarity between a random augmentation of a data sample and its instance-wise adversarial perturbation. We validate our method, Robust Contrastive Learning (RoCL), on multiple benchmark datasets, on which it obtains comparable robust accuracy over state-of-the-art supervised adversarial learning methods, and significantly improved robustness against the black box and unseen types of attacks. Moreover, with further joint fine-tuning with supervised adversarial loss, RoCL obtains even higher robust accuracy over using self-supervised learning alone. Notably, RoCL also demonstrate impressive results in robust transfer learning.
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Pr...
Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, as new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unseen nodes as in a conventional out-of-knowledge link prediction task but also between the unseen nodes, with only few edges per node. We tackle this problem with a novel transductive meta-learning framework which we refer to as Graph Extrapolation Networks (GEN). GEN meta-learns both the node embedding network for inductive inference (seen-to-unseen) and the link prediction network for transductive inference (unseen-to-unseen). For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction. The results show that our model significantly outperforms relevant baselines for out-of-graph link prediction tasks.
Neural Mask Generator : Learning to Generate Adaptive Word Maskings for Langu...
We propose a method to automatically generate a domain- and task-adaptive maskings of the given text for self-supervised pre-training, such that we can effectively adapt the language model to a particular target task (e.g. question answering). Specifically, we present a novel reinforcement learning-based framework which learns the masking policy, such that using the generated masks for further pre-training of the target language model helps improve task performance on unseen texts. We use off-policy actor-critic with entropy regularization and experience replay for reinforcement learning, and propose a Transformer-based policy network that can consider the relative importance of words in a given text. We validate our Neural Mask Generator (NMG) on several question answering and text classification datasets using BERT and DistilBERT as the language models, on which it outperforms rule-based masking strategies, by automatically learning optimal adaptive maskings.
Cost-effective Interactive Attention Learning with Neural Attention Process
We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IAL), in which the human supervisors interactively manipulate the allocated attentions, to correct the model's behavior by updating the attention-generating network. However, such a model is prone to overfitting due to scarcity of human annotations, and requires costly retraining. Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features. We tackle these challenges by proposing a sample-efficient attention mechanism and a cost-effective reranking algorithm for instances and features. First, we propose Neural Attention Process (NAP), which is an attention generator that can update its behavior by incorporating new attention-level supervisions without any retraining. Secondly, we propose an algorithm which prioritizes the instances and the features by their negative impacts, such that the model can yield large improvements with minimal human feedback. We validate IAL on various time-series datasets from multiple domains (healthcare, real-estate, and computer vision) on which it significantly outperforms baselines with conventional attention mechanisms, or without cost-effective reranking, with substantially less retraining and human-model interaction cost.
Adversarial Neural Pruning with Latent Vulnerability Suppression
Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challenging to deploy them in real-world safety-critical applications. In this paper, we conjecture that the leading cause of adversarial vulnerability is the distortion in the latent feature space, and provide methods to suppress them effectively. Explicitly, we define vulnerability for each latent feature and then propose a new loss for adversarial learning, Vulnerability Suppression (VS) loss, that aims to minimize the feature-level vulnerability during training. We further propose a Bayesian framework to prune features with high vulnerability to reduce both vulnerability and loss on adversarial samples. We validate our Adversarial Neural Pruning with Vulnerability Suppression (ANP-VS) method on multiple benchmark datasets, on which it not only obtains state-of-the-art adversarial robustness but also improves the performance on clean examples, using only a fraction of the parameters used by the full network. Further qualitative analysis suggests that the improvements come from the suppression of feature-level vulnerability.
Generating Diverse and Consistent QA pairs from Contexts with Information-Max...
One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer (QA) pairs for a target text domain with human annotation. An alternative approach to tackle the problem is to use automatically generated QA pairs from either the problem context or from large amount of unstructured texts (e.g. Wikipedia). In this work, we propose a hierarchical conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QA pairs to ensure their consistency. We validate our Information Maximizing Hierarchical Conditional Variational AutoEncoder (Info-HCVAE) on several benchmark datasets by evaluating the performance of the QA model (BERT-base) using only the generated QA pairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. The results show that our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distrib...
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies. Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness. To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task. Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning. We formulate this objective into a Bayesian inference framework and tackle it using variational inference. We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on two realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches. Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.
Meta Dropout: Learning to Perturb Latent Features for Generalization
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test examples, we may achieve better generalization performance. However, obtaining such perturbation is not possible in standard machine learning frameworks as the distribution of the test data is unknown. To tackle this challenge, we propose a novel regularization method, meta-dropout, which learns to perturb the latent features of training examples for generalization in a meta-learning framework. Specifically, we meta-learn a noise generator which outputs a multiplicative noise distribution for latent features, to obtain low errors on the test instances in an input-dependent manner. Then, the learned noise generator can perturb the training examples of unseen tasks at the meta-test time for improved generalization. We validate our method on few-shot classification datasets, whose results show that it significantly improves the generalization performance of the base model, and largely outperforms existing regularization methods such as information bottleneck, manifold mixup, and information dropout.
Scalable and Order-robust Continual Learning with Additive Parameter Decompos...
While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely varies based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameters for each task as a sum of task-shared and sparse task-adaptive parameters. With our Additive Parameter Decomposition (APD), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, we can achieve even better scalability with APD using hierarchical knowledge consolidation, which clusters the task-adaptive parameters to obtain hierarchically shared parameters. We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness
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Task Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive Learning
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint L...
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Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
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MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
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Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Pr...
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Neural Mask Generator : Learning to Generate Adaptive Word Maskings for Langu...
Neural Mask Generator : Learning to Generate Adaptive Word Maskings for Langu...
Cost-effective Interactive Attention Learning with Neural Attention Process
Cost-effective Interactive Attention Learning with Neural Attention Process
Adversarial Neural Pruning with Latent Vulnerability Suppression
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Generating Diverse and Consistent QA pairs from Contexts with Information-Max...
Generating Diverse and Consistent QA pairs from Contexts with Information-Max...
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distrib...
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distrib...
Meta Dropout: Learning to Perturb Latent Features for Generalization
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Scalable and Order-robust Continual Learning with Additive Parameter Decompos...
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Skill-Based Meta-Reinforcement Learning
- 1. Skill-Based Meta-Reinforcement Learning Taewook Nam Shao-Hua Sun Karl Pertsch Sung Ju Hwang Joseph J. Lim
- 2. Human Leverages Prior Knowledge “Cook a pancake”
- 3. Human Leverages Prior Knowledge “Cook a pancake” SAC Policy
- 4. Human Leverages Prior Knowledge “Cook a pancake” Prior knowledge Prior knowledge
- 5. Human Leverages Prior Knowledge “Cook a pancake” How to hold frying pan How to turn on the stove
- 6. Human Leverages Prior Knowledge “Cook a pancake” “Make a sandwich” “Fry an egg”
- 7. Human Leverages Prior Knowledge “Cook a pancake” “Make a sandwich” “Fry an egg” How to hold frying pan How to turn on the stove Skill-based RL Meta-RL
- 8. Skill-Based Reinforcement Learning[1, 2] Task-Agnostic Dataset How to turn on a stove How to hold a frying pan Skill [1] Accelerating Reinforcement Learning with learned Skill Prior. Pertsch et al. CoRL 2020 [2] Opal: O ffl ine Primitive Discovery for Accelerating O ffl ine Reinforcement Learning. Ajay et al. ICLR 2021
- 9. Skill-Based Reinforcement Learning[1, 2] Task-Agnostic Dataset How to turn on a stove How to hold a frying pan Skill [1] Accelerating Reinforcement Learning with learned Skill Prior. Pertsch et al. CoRL 2020 [2] Opal: O ffl ine Primitive Discovery for Accelerating O ffl ine Reinforcement Learning. Ajay et al. ICLR 2021 Reward
- 10. Skill-Based Reinforcement Learning[1, 2] T1 T2 T3 T4 TT T5 Target Task Task-Agnostic Dataset How to turn on a stove How to hold a frying pan Skill [1] Accelerating Reinforcement Learning with learned Skill Prior. Pertsch et al. CoRL 2020 [2] Opal: O ffl ine Primitive Discovery for Accelerating O ffl ine Reinforcement Learning. Ajay et al. ICLR 2021 + E ffi cient exploration Reward
- 11. T1 T2 T3 T4 TT T5 Target Task Task-Agnostic Dataset How to turn on a stove How to hold a frying pan Skill Skill-Based Reinforcement Learning[1, 2] [1] Accelerating Reinforcement Learning with learned Skill Prior. Pertsch et al. CoRL 2020 [2] Opal: O ffl ine Primitive Discovery for Accelerating O ffl ine Reinforcement Learning. Ajay et al. ICLR 2021 + E ffi cient exploration - How to learn quickly Reward
- 12. Meta Reinforcement Learning[1, 2] T1 T2 T3 T4 TT T5 Target Task Training Tasks T1 T2 T5 T3 T4 “Fry an egg” “Make a sandwich” [1] Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Finn et al. ICML 2017 [2] E ffi cient O ff -Policy Meta-reinforcement Learning via Probabilistic Context Variables. Rakelly et al. ICML 2019 “Cook a pancake” + How to learn quickly
- 13. T1 T2 T3 T4 TT T5 Target Task Training Tasks T1 T2 T5 T3 T4 “Fry an egg” “Cook a pancake” “Make a sandwich” Meta Reinforcement Learning[1, 2] [1] Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Finn et al. ICML 2017 [2] E ffi cient O ff -Policy Meta-reinforcement Learning via Probabilistic Context Variables. Rakelly et al. ICML 2019 + How to learn quickly - Limited to short-horizon task
- 14. This Work : Meta-RL + Skill-based RL Training Tasks T1 T2 T5 T3 T4 Task-Agnostic Dataset T1 T2 T3 T4 TT T5 Target Task
- 15. This Work : Meta-RL + Skill-based RL Useful skill T1 T2 T3 T4 TT T5 Target Task T1 T2 T5 T3 T4
- 16. Meta-RL + Skill-based RL How to learn quickly T1 T2 T3 T4 TT T5 Target Task T1 T2 T5 T3 T4
- 17. Skill-Based Meta-Reinforcement Learning T1 T2 T3 T4 TT T5 T1 T2 T5 T3 T4 Fast learning of new long horizon task
- 18. Skill-Based Meta-Reinforcement Learning T1 T2 T3 T4 TT T5 T1 T2 T5 T3 T4 Fast learning of new long horizon task SiMPL
- 19. Phase 1 : Skill Extraction Extract skill from task-agnostic o ffl ine data, following SPiRL[1]. Skill Task-Agnostic Data a0 a1 a2 a3 s0 s1 s2 s3 s4 … [1] Accelerating Reinforcement Learning with learned Skill Prior. Pertsch et al. CoRL 2020
- 20. Phase 2 : Skill-based Meta-training Meta-train based on extracted skill, following PEARL[1]. Meta Policy T1 T2 Meta-Training Tasks T5 T3 T4 Skill [1] E ffi cient O ff -Policy Meta-reinforcement Learning via Probabilistic Context Variables. Rakelly et al. ICML 2019
- 21. Phase 2 : Skill-based Meta-training Meta-train based on extracted skill, following PEARL[1]. Transitions Meta Policy Task Encoder T1 T2 Meta-Training Tasks T5 T3 T4 Skill [1] E ffi cient O ff -Policy Meta-reinforcement Learning via Probabilistic Context Variables. Rakelly et al. ICML 2019
- 22. Phase 3 : Target Task Learning Warm-start target task learning by task encoding. Target Task T1 T2 T3 T4 TT T5
- 23. Phase 3 : Target Task Learning Warm-start target task learning by task encoding. Initial Exploration Target Task T1 T2 T3 T4 TT T5 Task Encoder
- 24. Phase 3 : Target Task Learning Warm-start target task learning by task encoding. Policy Task Encoder Skill Target Task T1 T2 T3 T4 TT T5 Initial Exploration
- 25. Phase 3 : Target Task Learning Warm-start target task learning by task encoding. Policy Task Encoder Skill Target Task T1 T2 T3 T4 TT T5 Initial Exploration Fine-tune
- 26. Environment Maze Navigation 2000 steps / sparse reward for completion Kitchen Manipulation 280 steps / sparse reward for subtask completions
- 27. Environment Meta-Training Tasks Target Tasks arget Tasks Agent Meta-training Tasks Target Tasks top burner light switch slide cabinet hinge cabinet slide cabinet bottom burner bottom burner kettle bottom burner light switch top burner microwave kettle slide cabinet hinge cabinet light switch 1 2 3 4 vigation (b) Kitchen Manipulation Target Tasks Target Tasks rget Tasks Agent Meta-training Tasks Target Tasks top burner light switch slide cabinet hinge cabinet slide cabinet bottom burner bottom burner kettle bottom burner light switch top burner microwave kettle slide cabinet hinge cabinet light switch 1 2 3 4 vigation (b) Kitchen Manipulation Target Tasks Meta-Training Tasks
- 28. SiMPL Learns Quickly SiMPL can solve this task in 100 episode, but other baseline can’t. Meta-training Tasks Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Target Task Agent Trajectory Episode 0 Episode 20 Episode 100 SiMPL (Ours) SPiRL Episode 0 Episode 20 Episode 100 PEARL-ft MTRL Target Location Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Meta-training Tasks Agent Trajectory Start Location
- 29. SiMPL Learns Quickly SiMPL can solve this task in 100 episode, but other baseline can’t. Meta-training Tasks Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Target Task Agent Trajectory Episode 0 Episode 20 Episode 100 SiMPL (Ours) SPiRL Episode 0 Episode 20 Episode 100 PEARL-ft MTRL Target Location Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Meta-training Tasks Agent Trajectory Start Location
- 30. SiMPL Learns Quickly SiMPL can solve this task in 100 episode, but other baseline can’t. Meta-training Tasks Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Target Task Agent Trajectory Episode 0 Episode 20 Episode 100 SiMPL (Ours) SPiRL Episode 0 Episode 20 Episode 100 PEARL-ft MTRL Target Location Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Meta-training Tasks Agent Trajectory Start Location
- 31. SiMPL Learns Quickly SiMPL can solve this task in 100 episode, but other baseline can’t. Meta-training Tasks Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Target Task Agent Trajectory Episode 0 Episode 20 Episode 100 SiMPL (Ours) SPiRL Episode 0 Episode 20 Episode 100 PEARL-ft MTRL Target Location Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Meta-training Tasks Agent Trajectory Start Location
- 32. SiMPL Learns Quickly SiMPL can solve this task in 100 episode, but other baseline can’t. Meta-training Tasks Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Target Task Agent Trajectory Episode 0 Episode 20 Episode 100 SiMPL (Ours) SPiRL Episode 0 Episode 20 Episode 100 PEARL-ft MTRL Target Location Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Meta-training Tasks Agent Trajectory Start Location
- 33. SiMPL Learns Quickly SiMPL can solve this task in 100 episode, but other baseline can’t. Meta-training Tasks Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Target Task Agent Trajectory Episode 0 Episode 20 Episode 100 SiMPL (Ours) SPiRL Episode 0 Episode 20 Episode 100 PEARL-ft MTRL Target Location Episode 0 Episodes 20 Episodes 80 SPiRL Ours Meta-Training Task Target Task Meta-training Tasks Agent Trajectory Start Location
- 34. SiMPL Learns Quickly SiMPL converges faster than MTRL / Skill-based RL / Meta-RL baselines. SiMPL (Ours) SPiRL MTRL PEARL-ft SAC PEARL SiMPL (Ours) SPiRL MTRL PEARL-ft SAC PEARL
- 35. • SiMPL can leverage both o ffl ine dataset and tasks by combining skill-based RL and meta-RL • SiMPL can learn new long-horizon and sparse-reward tasks faster Summary
- 36. Summary • SiMPL can leverage both o ffl ine dataset and tasks by combining skill-based RL and meta-RL • SiMPL can learn new long-horizon and sparse-reward tasks faster
- 37. Skill-Based Meta-Reinforcement Learning Taewook Nam Shao-Hua Sun Karl Pertsch Sung Ju Hwang Joseph J. Lim Paper & Code : namsan96.github.io/SiMPL