改进深度强化学习对时变系统的鲁棒性：通过有界的极值搜索

On the Computability of Solomonoff Induction and Knowledge-Seeking

Solomonoff induction is held as a gold standard for learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking agents, and give a limit-computable weakly asymptotically optimal reinforcement learning agent.

面向安全强化学习的边界搜寻策略梯度算法

Boundary-Seeking Policy Gradient for Safe Reinforcement Learning

本文提出BSPG算法，针对安全强化学习中标准梯度法易收敛到可行域内部的问题，结合切向与法向分量，在Safety-Gymnasium导航任务中实现更高奖励与更紧密的边界跟踪。

WideSeek-R1: 探索通过多智能体强化学习进行广泛信息寻求的宽度扩展

WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

WideSeek-R1通过多智能体强化学习框架，实现广泛信息寻求任务中的宽度扩展，提升多智能体系统的协同与并行执行能力，实验表明其性能优于单智能体模型。

Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning

Vision-based reinforcement learning (RL) is a promising approach to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in terms of sample efficiency, especially when using image observations. This has led to increased attention on integrating state representation learning (SRL) techniques into the RL pipeline. Work in this field demonstrates a substantial improvement in sample efficiency among other benefits. However, to take full advantage of this paradigm, the quality of samples used for training plays a crucial role. More importantly,…

Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response

This paper introduces two metrics (cycle-based and memory-based metrics), grounded on a dynamical game-theoretic solution concept called sink equilibrium, for the evaluation, ranking, and computation of policies in multi-agent learning. We adopt strict best response dynamics (SBRD) to model selfish behaviors at a meta-level for multi-agent reinforcement learning. Our approach can deal with dynamical cyclical behaviors (unlike approaches based on Nash equilibria and Elo ratings), and is more compatible with single-agent reinforcement learning than alpha-rank which relies on weakly better respon…

Towards Information-Seeking Agents

We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals. We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks. We shape the behavior of these agents by combining extrinsic and intrinsic rewards. We empirically demonstrate that these agents learn to search actively a…

通过随机有界极值搜索实现生成模型的无梯度采样

Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking

提出一种无需梯度评估的生成模型采样方法，通过高频抖动余弦替代分数项，实现能量基和扩散模型的采样，适用于黑盒时变目标的潜空间场景。

Novel Policy Seeking with Constrained Optimization

In problem-solving, we humans can come up with multiple novel solutions to the same problem. However, reinforcement learning algorithms can only produce a set of monotonous policies that maximize the cumulative reward but lack diversity and novelty. In this work, we address the problem of generating novel policies in reinforcement learning tasks. Instead of following the multi-objective framework used in existing methods, we propose to rethink the problem under a novel perspective of constrained optimization. We first introduce a new metric to evaluate the difference between policies and then …