Ahmed M. Hendawy

The use of target networks is a popular approach for estimating value functions in deep Reinforcement Learning (RL). While effective, the target network remains a compromise solution that preserves stability at the cost of slowly moving targets, thus delaying learning. Conversely, using the online network as a bootstrapped target is intuitively appealing, albeit well-known to lead to unstable learning. In this work, we aim to obtain the best out of both worlds by introducing a novel update rule that computes the target using the MINimum estimate between the Target and Online network, giving rise to our method, MINTO. Through this simple, yet effective modification, we show that MINTO enables faster and stable value function learning, by mitigating the potential overestimation bias of using the online network for bootstrapping. Notably, MINTO can be seamlessly integrated into a wide range of value-based and actor-critic algorithms with a negligible cost. We evaluate MINTO extensively across diverse benchmarks, spanning online and offline RL, as well as discrete and continuous action spaces. Across all benchmarks, MINTO consistently improves performance, demonstrating its broad applicability and effectiveness.

Acquiring a universal policy that performs multiple tasks is a crucial building block in endowing agents with generalized capabilities. To this end, the field of Multi-Task Reinforcement Learning (MTRL) proposes sharing parameters and representations among tasks during the learning process. Still, optimizing for a single solution that is able to perform various skills remains challenging. Recent works attempt to address these challenges using a mixture of experts, though this comes at the cost of additional inference-time complexity. In this paper, we introduce STAR, a novel MTRL algorithm that leverages mode connectivity to share knowledge across single skills, while remaining parameter-efficient at deployment time. Particularly, we show that single-task policies can be linearly connected in policy parameter space to the multi-task policy, i.e., task performance is maintained throughout the linear path connecting the two policies. Our experimental evaluation demonstrates that mode connectivity at training time induces implicit regularization in the multi-task policy, surpassing related baselines on MTRL benchmarks MuJoCo and Metaworld. Furthermore, STAR achieves competitive performance even with methods that retain multiple models at inference time.

This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning.

Multi-Task Reinforcement Learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems. To this end, sharing representations plays a fundamental role in capturing both unique and common characteristics of the tasks. Tasks may exhibit similarities in terms of skills, objects, or physical properties while leveraging their representations eases the achievement of a universal policy. Nevertheless, the pursuit of learning a shared set of diverse representations is still an open challenge. In this paper, we introduce a novel approach for representation learning in MTRL that encapsulates common structures among the tasks using orthogonal representations to promote diversity. Our method, named Mixture Of Orthogonal Experts (MOORE), leverages a Gram-Schmidt process to shape a shared subspace of representations generated by a mixture of experts. When task-specific information is provided, MOORE generates relevant representations from this shared subspace. We assess the effectiveness of our approach on two MTRL benchmarks, namely MiniGrid and MetaWorld, showing that MOORE surpasses related baselines and establishes a new state-of-the-art result on MetaWorld.

Multimodal pretrained visual-language models (pVLMs) have showcased excellence across several applications, like visual question-answering. Their recent application for policy learning manifested promising avenues for augmenting robotic capabilities in the real world. This paper delves into the problem of parameter-efficient tuning of pVLMs for adapting them to robotic manipulation tasks with low-resource data. We showcase how Low-Rank Adapters (LoRA) can be injected into behavioral cloning temporal transformers to fuse language, multi-view images, and proprioception for multitask robot learning, even for long-horizon tasks. Preliminary results indicate our approach vastly outperforms baseline architectures and tuning methods, paving the way toward parameter-efficient adaptation of pretrained large multimodal transformers for robot learning with only a handful of demonstrations.

In this paper, we investigate the use of Proto Value Functions (PVFs) for measuring the similarity between tasks in the context of Curriculum Learning (CL). PVFs serve as a mathematical framework for generating basis functions for the state space of a Markov Decision Process (MDP). They capture the structure of the state space manifold and have been shown to be useful for value function approximation in Reinforcement Learning (RL). We show that even a few PVFs allow us to estimate the similarity between tasks. Based on this observation, we introduce a new algorithm called Curriculum Representation Policy Iteration (CRPI) that uses PVFs for CL, and we provide a proof of concept in a Goal-Conditioned Reinforcement Learning (GCRL) setting.

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.