Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction

11/28/2019
by   Vishal Jain, et al.
3

Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.

READ FULL TEXT

page 7

page 8

research
07/05/2018

Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks

Recent developments in deep reinforcement learning have enabled the crea...
research
06/15/2016

Deep Reinforcement Learning With Macro-Actions

Deep reinforcement learning has been shown to be a powerful framework fo...
research
09/15/2022

ProAPT: Projection of APT Threats with Deep Reinforcement Learning

The highest level in the Endsley situation awareness model is called pro...
research
12/03/2018

Towards Solving Text-based Games by Producing Adaptive Action Spaces

To solve a text-based game, an agent needs to formulate valid text comma...
research
01/09/2021

Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies

With the recent advancement in Deep Reinforcement Learning in the gaming...
research
09/11/2022

Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning

Deep reinforcement learning is a technique for solving problems in a var...
research
09/20/2019

A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning

We propose a planning and perception mechanism for a robot (agent), that...

Please sign up or login with your details

Forgot password? Click here to reset