Paper tackles skill transfer in RL for morphologically different agents.
problem Transfer skills between morphologically different reinforcement learning agents.
method Proposes a paired variational encoder-decoder model (PVED) for subspace learning.
result Demonstrates improved skill transfer efficiency compared to state-of-the-art methods.
New method automates asymmetric choice for better skill transfer in reinforcement learning.
problem Improving sample efficiency and transferability of reinforcement learning agents.
method Attentive Priors for Expressive and Transferable Skills (APES) using hierarchical KL-regularization.
result APES automates asymmetric choice, leading to better skill transfer across sequential tasks.
Robotics learns new skills faster by reusing past movements.
problem Learning new motor skills is time-consuming and requires exploration of a large space of motor configurations.
method Combines probabilistic movement primitives with relative entropy policy search for skill initialization and adaptation.
result Quality of learned skills improves and the number of required iterations to learn a new task can be reduced by more than 60%.
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are parameterized in a way that makes them easily reusable. This diversity and p…
Integrates skills and world models for efficient task solving and transfer.
problem Quickly solve new tasks in complex environments using reusable knowledge.
method Leverages partial amortization for fast adaptation and online skill planning.
result Improved sample efficiency in single tasks and transfer between tasks.
DSE learns transferable skills across changing dynamics and goals.
problem Learning transferable skills across different reinforcement learning tasks.
method Variational inference for multi-task reinforcement learning with shared and task-specific latent spaces.
result Policies can generalize to unseen dynamics and goals conditions.
HiPPO adapts skills and higher-level policies together for better transfer in hierarchical RL.
problem Sub-optimality in skill transfer when lower-level skills are fixed.
method HiPPO: a novel hierarchical policy gradient method that trains all levels of the hierarchy jointly.
result Improved robustness of skills to environment changes through training time-abstractions.
Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing policies which generalize across tasks, despite training for thousands of hours equivalent real robot time. To address this shortcoming, we…
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty of work on learning task-specific policies or skills but almost no focus on composing necessary, task-agnostic skills to find a solution to …
Framework for lifelong learning using eigentasks to avoid forgetting and transfer knowledge.
problem Avoiding forgetting and transferring knowledge in lifelong learning.
method Eigentask framework: skills paired with generative models, wake-sleep cycle for learning and consolidation.
result Improved performance in supervised continual learning, evidence of forward knowledge transfer.
Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To dis…
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each…
The paper proposes a method to transfer skills between tasks using disentangled latent policies.
problem Transfer learning in reinforcement learning struggles with diverse tasks without explicit supervision.
method Learning a small set of policies in a disentangled latent space that can be recombined to solve many tasks.
result Disentangled latent policies enable quick performance on many diverse tasks.
Humans are able to perform a myriad of sophisticated tasks by drawing upon skills acquired through prior experience. For autonomous agents to have this capability, they must be able to extract reusable skills from past experience that can be recombined in new ways for subsequent tasks. Furthermore, when controlling com…
The study shows how modular learning can adapt to new tasks.
problem Adapting to new tasks in an ever-changing environment.
method Task segmentation, modular learning, memory-based ensembling.
result The system demonstrates robustness to catastrophic forgetting and increasing positive transfer.
Paper learns versatile balancing and recovery motions for humanoid robots.
problem Training humanoid robots to handle unexpected perturbations.
method Hierarchical Deep Reinforcement Learning in a physics simulator.
result Learned skills comparable to preprogrammed controllers but more adaptable.
Agents learn social skills from each other, improving performance.
problem Independent RL agents fail to use social learning.
method Imposed constraints, model-based auxiliary loss, mixed training.
result Agents trained with social learning outperform solo-trained agents.
A new method for solving complex sequential decision-making problems by decomposing them into multiple levels.
problem Sequential decision-making with natural multi-level structure.
method Multi-level meta-reinforcement learning with skill-based curriculum.
result Efficiently reduces stochasticity and policy search space, leading to fewer iterations and computations.
Pre-trained language model boosts RL efficiency.
problem Low sample efficiency in RL, especially in lifelong learning.
method Use a pre-trained task-independent language model for transfer learning.
result Goal-conditional RL agents become more sample efficient.
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor sk…
Unified framework supports lifelong learning properties.
problem Capturing desirable properties of human learning in machine learning.
method Unified framework with one central mechanism.
result Supports almost all desirable properties of human learning.
This work proposes a RL approach to learn versatile robotic manipulation tasks.
problem Challenging manipulation tasks in robotics and vision.
method Reinforcement learning (RL) to combine primitive skills, no intermediate rewards, few demonstrations, and efficient skill learning.
result Versatile robotic manipulation in challenging settings with temporary occlusions and dynamic scene changes.
A powerful network teaches a weak one, improving its performance.
problem Improving the performance of a weak neural network using a more powerful one.
method During training, a weak network learns features from a strong network to minimize feature distance.
result A weak neural network can increase its performance by learning from a more powerful network.
ReSkill reconciles RL skill creation with policy optimization.
problem RL policies lack reusable strategies across tasks.
method Integrates skill creation into RL loop with three mechanisms.
result Consistently outperforms existing methods, especially on unseen tasks.
Hierarchical MARL learns complementary skills for team coordination.
problem Creating intelligent agents that coordinate like human sports teams.
method Two-level hierarchical MARL with unsupervised skill discovery.
result Emergence of useful and complementary skills in team games.
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian moti…
Offline Signature Verification (OSV) remains a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during the training. This challenge is aggravated when there are small labeled training data available but with large intra-personal variations. In this study, we a…
Machine learning speeds up CRTM model predictions for weather forecasting.
problem Insufficient computational efficiency of radiative transfer models.
method Probabilistic neural network emulator of CRTM.
result Emulator predicts brightness temperatures with RMSE < 0.1 K for clear sky conditions.
Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also de…
Bayesian nonparametrics enables dynamic skill discovery from expert data.
problem Fixed K for offline skill discovery in reinforcement learning.
method Variational inference, continuous relaxations, Bayesian nonparametrics.
result Nonparametric model with dynamically-changing number of options.
Paper learns skill distributions from game outcomes, proving minimax optimality.
problem Learning skill distributions from noisy pairwise game outcomes.
method Proposes a simple algorithm using kernel density estimation and minimax techniques.
result Near-optimal minimax mean squared error scaling for skill density estimation.
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other …
Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. Howeve…
The paper provides fast, sample-efficient algorithms for learning common features in multi-task linear regression.
problem Learning transferable features from multiple related tasks.
method Provably fast, sample-efficient algorithms for multi-task linear regression.
result Theoretical guarantees on sample complexity for learning common features.
We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up t…
CausalWorld benchmarks robotic manipulation tasks with causal structure for transfer learning.
problem Challenges in transferring learned skills to new robotic manipulation environments.
method Proposes a simulation-based benchmark with a combinatorial family of tasks.
result Demonstrates the feasibility of tasks in the benchmark and provides baseline results.
The paper explains how language models acquire complex skills through scaling laws and statistical analysis.
problem Understanding how language models acquire new skills as their size and training data increase.
method Statistical framework and mathematical analysis of scaling laws.
result Language models can learn complex skills efficiently due to a strong inductive bias.
Convolutional neural networks improve surgical skill evaluation.
problem Manual feedback from senior surgeons is laborious, expensive, and subjective.
method Fully convolutional neural networks (CNNs) for classifying and regressing surgical skills.
result Deep neural networks achieve competitive performance on JIGSAWS dataset.
Researchers study how skills are learned in neural networks using physics principles.
problem Understanding how skills are sequentially learned in neural networks.
method Abstract and simplify the problem using physics principles, proposing three models: Geometry, Resource, and Domino.
result Models reveal insights into neural scaling laws, learning dynamics, and the benefits of modularity.
The paper examines skill estimation and variance under model misspecification in IRT.
problem Underestimation and overestimation of skills when non-compensatory model is misspecified as compensatory.
method Theoretical approach to analyze underestimation and overestimation of skills and variance.
result Overestimation of skills occurs around the origin and asymptotic variance differs under model misspecification.
ABC method improves subseasonal weather forecasting by 60-90%.
problem Improving subseasonal temperature and precipitation forecasting accuracy.
method Combines dynamical forecasts with machine learning-based bias correction.
result Significant improvement in temperature and precipitation forecasting skills.
Training on mixed distributions improves test performance even when components are unrelated.
problem Improving test performance with mismatched training and test distributions.
method Analyzing mixture distributions with different training and test proportions.
result Distribution shift can be beneficial, improving test performance even when components are unrelated.
A planning approach learns skills from interactions, balancing exploration and exploitation.
problem Learning robust high-level skills in noisy environments with unknown pre-conditions.
method Formulates skills as high-level policies, learns plans via bandit problems, balances exploration and exploitation.
result A planner capable of learning robust high-level skills in high-dimensional state spaces.
Improves AI agents' 3D navigation by learning from failures and 3D spatial relationships.
problem Challenges in data efficiency, obstacle avoidance, and generalization in 3D visual navigation.
method Incorporates attention on 3D spatial relationships and a target skill extension module into DRL framework.
result Significantly improves navigation performance and generalization across targets and scenes.
DADS discovers skills with predictable outcomes from unlabeled data.
problem Learning accurate models for complex dynamical systems is difficult and often doesn't generalize well.
method Dynamics-Aware Discovery of Skills (DADS) combines model-based and model-free learning.
result DADS discovers infinitely many behaviors in high-dimensional state-spaces.
Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just …
New algorithm trains neural nets on simple skills to learn complex tasks faster.
problem Learning complex tasks through simple imitation.
method Train neural networks on simple, easy-to-learn skills to accelerate learning of complex, hard-to-learn tasks.
result Consistently outperforms state-of-the-art baseline in training speed and performance.