Intrinsic Reward

[TOC] Title: Hierarchies of Reward Machines Author: Daniel Furelos-Blanco et. al. Publish Year: 4 Jun 2023 Review Date: Fri, Apr 12, 2024 url: https://arxiv.org/abs/2205.15752 Summary of paper Motivation Finite state machine are a simple yet powerful formalism for abstractly representing temporal tasks in a structured manner. Contribution The work introduces Hierarchies of Reinforcement Models (HRMs) to enhance the abstraction power of existing models. Key contributions include: HRM Abstraction Power: HRMs allow for the creation of hierarchies of Reinforcement Models (RMs), enabling constituent RMs to call other RMs. It’s proven that any HRM can be converted into an equivalent flat HRM with identical behavior. However, the equivalent flat HRM can have significantly more states and edges, especially under specific conditions. ...

[TOC] Title: DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards Author: Shanchuan Wan et. al. Publish Year: 18 May 2023 Review Date: Fri, Apr 12, 2024 url: https://arxiv.org/abs/2304.10770 Summary of paper Motivation Recent studies have shown the effectiveness of encouraging exploration with intrinsic rewards estimated from novelties in observations However, there is a gap between the novelty of an observation and an exploration, as both the stochasticity in the environment and agent’s behaviour may affect the observation. Contribution we propose DEIR, a novel method in which we theoretically derive an intrinsic reward with a conditional mutual information term that principally scales with the novelty contributed by agent explorations, and then implement the reward with a discriminative forward model. want to design a novel intrinsic reward design that considers not only the observed novelty but also the effective contribution brought by the agent. Some key terms internal rewards ...