While deep generative models are showing exciting abilities in computer
vision and natural language processing, their adoption in communication
frameworks is still far underestimated. These methods are demonstrated to
evolve solutions to classic communication problems such as denoising,
restoration, or compression. Nevertheless, generative models can unveil their
real potential in semantic communication frameworks, in which the receiver is
not asked to recover the sequence of bits used to encode the transmitted
(semantic) message, but only to regenerate content that is semantically
consistent with the transmitted message. Disclosing generative models
capabilities in semantic communication paves the way for a paradigm shift with
respect to conventional communication systems, which has great potential to
reduce the amount of data traffic and offers a revolutionary versatility to
novel tasks and applications that were not even conceivable a few years ago. In
this paper, we present a unified perspective of deep generative models in
semantic communication and we unveil their revolutionary role in future
communication frameworks, enabling emerging applications and tasks. Finally, we
analyze the challenges and opportunities to face to develop generative models
specifically tailored for communication systems.

通过深度生成模型在语义通信中的应用，揭示了其在未来通信框架中的革命性作用，为新兴应用和任务提供了前所未有的机会，并分析了专门针对通信系统开发生成模型的挑战和机遇。

生成 AI 遇见语义传播：通信任务的演变与革命

Generative AI Meets Semantic Communication: Evolution and Revolution of  Communication Tasks

Decision Transformers (DT) have demonstrated strong performances in offline
reinforcement learning settings, but quickly adapting to unseen novel tasks
remains challenging. To address this challenge, we propose a new framework,
called Hyper-Decision Transformer (HDT), that can generalize to novel tasks
from a handful of demonstrations in a data- and parameter-efficient manner. To
achieve such a goal, we propose to augment the base DT with an adaptation
module, whose parameters are initialized by a hyper-network. When encountering
unseen tasks, the hyper-network takes a handful of demonstrations as inputs and
initializes the adaptation module accordingly. This initialization enables HDT
to efficiently adapt to novel tasks by only fine-tuning the adaptation module.
We validate HDT's generalization capability on object manipulation tasks. We
find that with a single expert demonstration and fine-tuning only 0.5% of DT
parameters, HDT adapts faster to unseen tasks than fine-tuning the whole DT
model. Finally, we explore a more challenging setting where expert actions are
not available, and we show that HDT outperforms state-of-the-art baselines in
terms of task success rates by a large margin.

Hyper-Decision Transformer (HDT) framework proposes an adaptation module initialized by a hyper-network to enable efficient adaptation to novel tasks for offline reinforcement learning, demonstrating better performance than state-of-the-art baselines in terms of task success rates.

高效在线策略适应的超级决策 Transformer

Hyper-Decision Transformer for Efficient Online Policy Adaptation

We describe a parametrized space for simple meta-reinforcement-learning
(meta-RL) tasks with arbitrary stimuli. The parametrization allows us to
randomly generate an arbitrary number of novel simple meta-learning tasks. The
space of meta-RL tasks covered by this parametrization includes many well-known
meta-RL tasks, such as bandit tasks, the Harlow task, T-mazes, the Daw two-step
task and others. Simple extensions allow it to capture tasks based on
two-dimensional topological spaces, such as find-the-spot or key-door tasks. We
describe a number of randomly generated meta-RL tasks and discuss potential
issues arising from random generation.

利用参数化空间随机生成简单元元强化学习任务，覆盖许多已知的任务，并能对二维拓扑任务进行扩展。作者描述了几个随机生成的元元强化学习任务，并讨论相关问题。

元 - 强化学习任务的大规模参数化空间

A large parametrized space of meta-reinforcement learning tasks

Successful deployment of multi-agent reinforcement learning often requires
agents to adapt their behaviour. In this work, we discuss the problem of
teamwork adaptation in which a team of agents needs to adapt their policies to
solve novel tasks with limited fine-tuning. Motivated by the intuition that
agents need to be able to identify and distinguish tasks in order to adapt
their behaviour to the current task, we propose to learn multi-agent task
embeddings (MATE). These task embeddings are trained using an encoder-decoder
architecture optimised for reconstruction of the transition and reward
functions which uniquely identify tasks. We show that a team of agents is able
to adapt to novel tasks when provided with task embeddings. We propose three
MATE training paradigms: independent MATE, centralised MATE, and mixed MATE
which vary in the information used for the task encoding. We show that the
embeddings learned by MATE identify tasks and provide useful information which
agents leverage during adaptation to novel tasks.

本文提出了一种使用多智能体任务嵌入（MATE）解决团队合作适应性问题的新方法，使用深度学习技术将任务嵌入到低维空间中进行编码和解码，利用 MATE 训练范例建立三种任务编码模型，通过分析测试结果发现，MATE 学习的任务嵌入鲜明区分不同的任务，并能在多任务情境下产生非常好的适应性效果。