Search Results for author:
Found 5 papers, 2 papers with code
Opti Code Pro: A Heuristic Search-based Approach to Code Refactoring
This paper presents an approach that evaluates best-first search methods to code refactoring.
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning
Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems.
Partially Observable Reinforcement Learning
Problem Decomposition
+2
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
First, we propose reward machines, a type of finite state machine that supports the specification of reward functions while exposing reward function structure.
The act of remembering: a study in partially observable reinforcement learning
Learning memoryless policies is efficient and optimal in fully observable environments.
Partially Observable Reinforcement Learning
reinforcement-learning
+1
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
In this paper we propose Reward Machines {—} a type of finite state machine that supports the specification of reward functions while exposing reward function structure to the learner and supporting decomposition.
