Search Results for author:
Found 18 papers, 5 papers with code
PRP Rebooted: Advancing the State of the Art in FOND Planning
Fully Observable Non-Deterministic (FOND) planning is a variant of classical symbolic planning in which actions are nondeterministic, with an action's outcome known only upon execution.
Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
In this paper we investigate the notion of fairness in the context of sequential decision making where multiple stakeholders can be affected by the outcomes of decisions.
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior
Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR.
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions.
Learning to Follow Instructions in Text-Based Games
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language.
Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks
Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi.
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
Be Considerate: Objectives, Side Effects, and Deciding How to Act
We endow RL agents with the ability to contemplate such impact by augmenting their reward based on expectation of future return by others in the environment, providing different criteria for characterizing impact.
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning
We introduce an RL-based framework for learning to accomplish tasks in mobile apps.
Planning from Pixels using Inverse Dynamics Models
Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents.
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.
Interpretable Sequence Classification via Discrete Optimization
Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance.
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
Towards the Role of Theory of Mind in Explanation
Theory of Mind is commonly defined as the ability to attribute mental states (e. g., beliefs, goals) to oneself, and to others.
Towards Neural-Guided Program Synthesis for Linear Temporal Logic Specifications
Synthesizing a program that realizes a logical specification is a classical problem in computer science.
Towards Empathetic Planning
Critical to successful human interaction is a capacity for empathy - the ability to understand and share the thoughts and feelings of another.
Finite LTL Synthesis with Environment Assumptions and Quality Measures
In this paper, we investigate the problem of synthesizing strategies for linear temporal logic (LTL) specifications that are interpreted over finite traces -- a problem that is central to the automated construction of controllers, robot programs, and business processes.
Finite LTL Synthesis is EXPTIME-complete
LTL synthesis -- the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applications.
