arXiv research

Deep RL optimizes goal-based investing strategies.

The paper proposes a method to transfer skills between tasks using disentangled latent policies.

New approach to goal-based investing using hedging and reinforcement learning.

Transfer learning across different reinforcement learning (RL) tasks is becoming an increasingly valuable area of research. We consider a goal-based multi-task RL framework and mechanisms by which previously solved tasks can reduce sample complexity and regret when the agent is faced with a new task. Specifically, we i…

Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learning, state representation learning can help learn a compact, efficient and relevant representation of states that speeds up policy learning, …

A framework for goal-based investing with penalties for fund transfers.

New method predicts vehicle trajectories using map lane centers.

Reinforcement learning for optimizing retirement plans and target dated funds.

Investor aims to meet financial goals with deadlines and target amounts, considering stock trading costs.

Despite increasing attention paid to the need for fast, scalable methods to analyze next-generation neuroscience data, comparatively little attention has been paid to the development of similar methods for behavioral analysis. Just as the volume and complexity of brain data have grown, behavioral paradigms in systems n…

KEMP predicts long-term trajectories for autonomous driving using keyframes.

The paper introduces deep learning for ALM, enhancing asset and liability management.

According to Dennett, the same system may be described using a `physical' (mechanical) explanatory stance, or using an `intentional' (belief- and goal-based) explanatory stance. Humans tend to find the physical stance more helpful for certain systems, such as planets orbiting a star, and the intentional stance for othe…

Many reinforcement-learning researchers treat the reward function as a part of the environment, meaning that the agent can only know the reward of a state if it encounters that state in a trial run. However, we argue that this is an unnecessary limitation and instead, the reward function should be provided to the learn…

Vanguard uses AI to create personalized financial plans.

Semi-analytical approach for optimal wealth management contributions.

New method optimizes treatment policies to avoid winner's curse.

Method identifies galaxies with recent star formation variations.

This paper reviews dMTL and methods for selecting auxiliary tasks.

APT-Gen generates tasks to help RL learn in hard problems.

OCEAN infers online task identities from context variables.

Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between …

Proposes a new method for clustering tasks in multi-task learning.

Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a n…

HydaLearn dynamically adjusts task weights for better MTL performance.

New distance metric for neural architecture search reduces search space complexity.

HGNN learns augmented features for deep multi-task learning.

We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario. Although this matrix provides us critical information regarding similarity betw…

ST-MAML tackles task ambiguity in meta-learning by encoding tasks with stochastic representations.

New algorithm combines curriculum learning with HER for complex object manipulation tasks.

Transformers infer tasks from context via two modes, geometrically shaped task vectors explain their behavior.

MTL-NAS combines NAS with GP-MTL for task-agnostic multi-task learning.

Meta-learning framework uses task similarity through nonparametric kernel regression.

Novel meta-RL strategy improves efficiency in learning novel tasks.

Study shows perceptual boost of visual attention varies with task difficulty and size.

RTE enables extrapolation to new tasks by learning task transformations.

In this paper we investigate two hypothesis regarding the use of deep reinforcement learning in multiple tasks. The first hypothesis is driven by the question of whether a deep reinforcement learning algorithm, trained on two similar tasks, is able to outperform two single-task, individually trained algorithms, by more…

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as second…

Robot learns multiple tasks hierarchically by transferring knowledge.

Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r.t. the primary task is not known a priori, the success of MTL models depends on th…

In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nat…

Transformers can generalize to a large task family with only a few demonstrations.

Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constrain…

Framework adapts to new tasks based on prior knowledge.

Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization. Recent efforts apply meta-learning to learn a meta-learner from a set of RL tasks such that a novel but related task could be solved quickly. Though specific in some ways, different tasks in meta-RL…

Gradient surgery improves multi-task learning efficiency.

We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks sampled from the same distribution. The task identities of the unseen tasks are not provided. To perform well, the policy must infer the tas…

Method reweights auxiliary tasks to reduce data need for main task.