VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation

Universal Successor Features Approximators

The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks into simpler ones, the exchange of information between tasks, and the reuse of skills. We focus on one aspect in particular, namely the ability to generalise to unseen tasks. Parametric generalisation relies on the interpolation power of a function approximator that is given the task description as input; one of its most common form are universal value function approximators (UVFAs). Another way to generalise to new tas…

State2vec: Off-Policy Successor Features Approximators

A major challenge in reinforcement learning (RL) is the design of agents that are able to generalize across tasks that share common dynamics. A viable solution is meta-reinforcement learning, which identifies common structures among past tasks to be then generalized to new tasks (meta-test). In meta-training, the RL agent learns state representations that encode prior information from a set of tasks, used to generalize the value function approximation. This has been proposed in the literature as successor representation approximators. While promising, these methods do not generalize well acros…

用于强化学习安全性的堆叠式通用后继特征近似器

Stacked Universal Successor Feature Approximators for Safety in Reinforcement Learning

本文提出 SUSFAS，将适配 SAC 的堆叠连续控制 USFA 与多种次级安全控制器结合，以在高风险强化学习任务中保障安全并改善次级目标性能。

Universal Successor Representations for Transfer Reinforcement Learning

The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function (Sutton et al., 2011) has been shown to be useful for knowledge transfer, learning a universal value function can be challenging in practice. To attack this, we propose (1) to use universal successor representations (USR) to represent the transferable knowledge and (2) a USR approximator (USRA) that can be trained by interacting with…

基于模块化后继特征近似器的任务知识组合

Composing Task Knowledge with Modular Successor Feature Approximators

针对SF&GPI框架依赖人工设计状态特征的问题，提出模块化后继特征近似器MSFA架构，可自主发现预测目标并学习预测表示，实现更优的任务知识组合与泛化能力。

智能体策略组合是否安全？重新思考合作多智能体强化学习中的后继特征迁移

Is Per-Agent Policy Composition Safe? Rethinking Successor-Feature Transfer in Cooperative Multi-Agent Reinforcement Learning

针对多智能体强化学习中独立策略组合不安全的问题，提出MA-USFA分层方法，兼顾策略组合的安全性与灵活性，无需任务适配即可部署。