We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.
They test their approach on 2 environments -- Homeworld and Fantasyworld -- using the Evennia toolkit http: www.evennia.com . Homeworld is a small environment with 4 rooms, a vocabulary of 84 words, and 4 quests. Quests are also specified through text; for example, Not you are sleepy now but you are hungry now (which indicates that the player should obtain food but should not get into bed). Fantasyworld is much larger, with a vocabulary size of 1340, and has stochastic state transitions. The LSTM-DQN completes 100 @cite_1 introduce the Deep Reinforcement Relevance Network (DRRN) for tackling choice-based text games. They evaluate the DRRN on a deterministic game called Saving John'' and a larger-scale stochastic game called Machine of Death''. These games have vocabulary sizes 1762 and 2258 and action vocabulary sizes of 171 and 419, respectively. The DRRN takes as input the observation @math of the state @math and action choices @math and computes a Q-value for each possible pair:
@cite_1
2121863487
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.