Model-based Offline Policy Optimization with Adversarial Network

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model's uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately.Comment: Accepted by 26th European Conference on Artificial Intelligence ECAI 202

Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models

Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability. This ability could be applied to building babylike models, i.e. models at small scales, improving training efficiency. In this paper, we propose a "CoThought" pipeline, which efficiently trains smaller "baby" language models (BabyLMs) by leveraging the Chain of Thought prompting of LLMs. Our pipeline restructures a dataset of less than 100M in size using GPT-3.5-turbo, transforming it into task-oriented, human-readable texts that are comparable to the school texts for language learners. The BabyLM is then pretrained on this restructured dataset in a RoBERTa fashion. In evaluations across 4 benchmarks, our BabyLM outperforms the vanilla RoBERTa in 10 linguistic, NLU, and question-answering tasks by more than 3 points, showing a superior ability to extract contextual information. These results suggest that compact LMs pretrained on small, LLM-restructured data can better understand tasks and achieve improved performance.Comment: CoNLL 2023 BabyLM Challeng

Propagation of elastic solitons in chains of pre-deformed beams

International audienceWe use a combination of experiments, numerical analysis and theory to investigate the nonlinear dynamic response of a chain of precompressed elastic beams. Our results show that this simple system offers a rich platform to study the propagation of large amplitude waves. Compression waves are strongly dispersive, whereas rarefaction pulses propagate in the form of solitons. Further, we find that the model describing our structure closely resembles those introduced to characterize the dynamics of several molecular chains and macromolecular crystals, suggesting that our macroscopic system can provide insights into the effect of nonlinear vibrations on molecular mechanisms

MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

Driving safely requires multiple capabilities from human and intelligent agents, such as the generalizability to unseen environments, the safety awareness of the surrounding traffic, and the decision-making in complex multi-agent settings. Despite the great success of Reinforcement Learning (RL), most of the RL research works investigate each capability separately due to the lack of integrated environments. In this work, we develop a new driving simulation platform called MetaDrive to support the research of generalizable reinforcement learning algorithms for machine autonomy. MetaDrive is highly compositional, which can generate an infinite number of diverse driving scenarios from both the procedural generation and the real data importing. Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic. The generalization experiments conducted on both procedurally generated scenarios and real-world scenarios show that increasing the diversity and the size of the training set leads to the improvement of the generalizability of the RL agents. We further evaluate various safe reinforcement learning and multi-agent reinforcement learning algorithms in MetaDrive environments and provide the benchmarks. Source code, documentation, and demo video are available at https://metadriverse.github.io/metadrive . More research projects based on MetaDrive simulator are listed at https://metadriverse.github.ioComment: Source code, documentation, and demo video are available at https://metadriverse.github.io/metadrive . More research projects based on MetaDrive simulator are listed at https://metadriverse.github.i

GAN Inversion: A Survey

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator. As an emerging technique to bridge the real and fake image domains, GAN inversion plays an essential role in enabling the pretrained GAN models such as StyleGAN and BigGAN to be used for real image editing applications. Meanwhile, GAN inversion also provides insights on the interpretation of GAN's latent space and how the realistic images can be generated. In this paper, we provide an overview of GAN inversion with a focus on its recent algorithms and applications. We cover important techniques of GAN inversion and their applications to image restoration and image manipulation. We further elaborate on some trends and challenges for future directions

Decentralized Policy Coordination in Mobile Sensing with Consensual Communication

In a typical mobile-sensing scenario, multiple autonomous vehicles cooperatively navigate to maximize the spatial–temporal coverage of the environment. However, as each vehicle can only make decentralized navigation decisions based on limited local observations, it is still a critical challenge to coordinate the vehicles for cooperation in an open, dynamic environment. In this paper, we propose a novel framework that incorporates consensual communication in multi-agent reinforcement learning for cooperative mobile sensing. At each step, the vehicles first learn to communicate with each other, and then, based on the received messages from others, navigate. Through communication, the decentralized vehicles can share information to break through the dilemma of local observation. Moreover, we utilize mutual information as a regularizer to promote consensus among the vehicles. The mutual information can enforce positive correlation between the navigation policy and the communication message, and therefore implicitly coordinate the decentralized policies. The convergence of this regularized algorithm can be proved theoretically under certain mild assumptions. In the experiments, we show that our algorithm is scalable and can converge very fast during training phase. It also outperforms other baselines significantly in the execution phase. The results validate that consensual communication plays very important role in coordinating the behaviors of decentralized vehicles