Search Results for author:
Found 10 papers, 3 papers with code
Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure
We present counting reward automata-a finite state machine variant capable of modelling any reward function expressible as a formal language.
ROSARL: Reward-Only Safe Reinforcement Learning
A common solution is for a human expert to define either a penalty in the reward function or a cost to be minimised when reaching unsafe states.
World Value Functions: Knowledge Representation for Learning and Planning
We propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an agent's environment.
Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
The agent can then flexibly compose them both logically and temporally to provably achieve temporal logic specifications in any regular language, such as regular fragments of linear temporal logic.
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
In this work we propose world value functions (WVFs), which are a type of general value function with mastery of the world - they represent not only how to solve a given task, but also how to solve any other goal-reaching task.
Hierarchical Reinforcement Learning with AI Planning Models
Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP).
Learning to Follow Language Instructions with Compositional Policies
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions.
Generalisation in Lifelong Reinforcement Learning through Logical Composition
We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether a new task can be immediately solved using its existing abilities, or whether a task-specific skill should be learned.
Logical Composition in Lifelong Reinforcement Learning
The ability to produce novel behaviours from existing skills is an important property of lifelong learning agents.
A Boolean Task Algebra for Reinforcement Learning
The ability to compose learned skills to solve new tasks is an important property of lifelong-learning agents.
