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Found 8 papers, 2 papers with code
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.
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.
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.
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
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.
