A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents | Paper Citation Record

Observation 1a07417e-33f3-49ae-ba5b-d399a42dee25 · outbound

This paper cites However, the development of a framework to assess the impact of PCG techniques when integrated into SGs remains particularly challenging.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents However, the development of a framework to assess the impact of PCG techniques when integrated into SGs remains particularly challenging

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:37.154542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 33e2cddb-04ae-4de3-b4b2-f17d18f38710 · outbound

This paper cites The agents are then trained against three SG versions and checkpoints are saved throughout training.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents The agents are then trained against three SG versions and checkpoints are saved throughout training

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:36.505930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d4cd701f-1581-4f6d-8e94-3d602eecd798 · outbound

This paper cites ToG-2024-0198.R3 7 The Scenario-Based test pits the best agent trained instances from all versions against NPCs generated randomly (version.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents ToG-2024-0198.R3 7 The Scenario-Based test pits the best agent trained instances from all versions against NPCs generated randomly (version

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:36.167491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb5b19fa-b175-4438-ac15-16e14f4eeb00 · outbound

This paper cites Wake Up for the Future.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Wake Up for the Future

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:36.679439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 125c86c3-8032-4f07-8246-7f875e563324 · outbound

This paper cites Rising to the Challenge: An Emotion-Driven Approach Toward Adaptive Serious Games,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Rising to the Challenge: An Emotion-Driven Approach Toward Adaptive Serious Games,

Reference 5

Resolution: verified exact; doi, observed 2026-08-07T14:57:31.549413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f2ee70dd-aead-4493-9ad5-ceba941b434a · outbound

This paper cites Wake Up for the Future.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Wake Up for the Future

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:35.859270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a79e5916-39d7-459a-8b84-c4dbe45fe0fc · outbound

This paper cites The expected benefits of the procedurally generated game environments trade off against the great challenge of evaluating them [9].

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents The expected benefits of the procedurally generated game environments trade off against the great challenge of evaluating them [9]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:36.995532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 200d7a5b-5e52-451a-8656-3fe9fe36b716 · outbound

This paper cites Dashed lines show the average win rate and regular lines show the max win rate of the agent instances trained for the respective number of SGAs.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Dashed lines show the average win rate and regular lines show the max win rate of the agent instances trained for the respective number of SGAs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:35.998341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3fbbf9d6-377d-4025-8f0d-65ac6c27cd20 · outbound

This paper cites What is procedural content generation? Mario on the borderline,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents What is procedural content generation? Mario on the borderline,

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:57:34.172633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2b2fd49a-4c52-492d-bcd7-f632bc137ef9 · outbound

This paper cites Experience-Driven Procedural Content Generation,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Experience-Driven Procedural Content Generation,

Reference 11

Resolution: verified exact; doi, observed 2026-08-07T14:57:31.722013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 372a61ef-01be-4b67-98c2-e9de75a5b517 · outbound

This paper cites Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms,

Reference 12

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:57:33.900148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 02d7ff06-298c-4876-8f8c-977ae6b6a5e7 · outbound

This paper cites Classifying serious games: the G/P/S model,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Classifying serious games: the G/P/S model,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:35.549835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eae57d38-e4ee-4196-8dca-2e80e4a8734d · outbound

This paper cites Deep learning, reinforcement learning, and world models,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Deep learning, reinforcement learning, and world models,

Reference 14

Resolution: verified exact; doi, observed 2026-08-07T14:57:31.255677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fa75365b-4565-436a-a672-1be605fa53ff · outbound

This paper cites An Ontology for Personalization in Serious Games for Assessment,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents An Ontology for Personalization in Serious Games for Assessment,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:24.789151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ae6a80a-2203-45a3-b956-9eba944a4c88 · outbound

This paper cites 2010, pp.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents 2010, pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:35.239844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 77efeaef-9c04-4800-b4a4-cf2fd506d871 · outbound

This paper cites Serious Interactive Digital Narrative: Explorations in Personalization and Player Experience Enrichment,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Serious Interactive Digital Narrative: Explorations in Personalization and Player Experience Enrichment,

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-07T14:57:33.574844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 62e51f64-fcd9-4396-bff5-50c9874752d8 · outbound

This paper cites Synchronizing Game and AI Design in PCG-Based Game Prototypes,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Synchronizing Game and AI Design in PCG-Based Game Prototypes,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:25.016806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07538ca8-b7b9-4948-821c-8ac3df6162cc · outbound

This paper cites Deep learning for procedural content generation,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Deep learning for procedural content generation,

Reference 20

Resolution: verified exact; doi, observed 2026-08-07T14:57:31.365766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 74b2cc16-3f06-467a-af72-a31927b6b13f · outbound

This paper cites Human-Like Playtesting with Deep Learning,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Human-Like Playtesting with Deep Learning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:25.230791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 091202e0-264a-4491-ab69-5b3c2ad15dc2 · outbound

This paper cites Systematic Review of Dynamic Difficulty Adaption for Serious Games: The Importance of Diverse Approaches,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Systematic Review of Dynamic Difficulty Adaption for Serious Games: The Importance of Diverse Approaches,

Reference 22

Resolution: verified exact; doi, observed 2026-08-07T14:57:30.609065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 090f9bf6-2848-42dc-9f59-8ff57c017995 · outbound

This paper cites Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:26.267861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a05e256-0903-44e9-b628-fdc9f2fb5fcd · outbound

This paper cites Proximal Policy Optimization Algorithms.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Proximal Policy Optimization Algorithms

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:25.244812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3c9476e-7182-4565-84cb-a7f915fb8093 · outbound

This paper cites Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:57:30.850392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 676a76b6-3fcb-42cc-9510-58c4ce7be544 · outbound

This paper cites Human-level control through deep reinforcement learning,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Human-level control through deep reinforcement learning,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:25.382126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 935bb059-eeda-404e-ae00-53191f67904d · outbound

This paper cites Machine versus Human Attention in Deep Reinforcement Learning Tasks,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Machine versus Human Attention in Deep Reinforcement Learning Tasks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:34.957053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c1ac59f1-dc63-4d6c-90d7-caef591da5fb · outbound

This paper cites Review of Intrinsic Motivation in Simulation-based Game Testing,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Review of Intrinsic Motivation in Simulation-based Game Testing,

Reference 28

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-07T14:57:32.810198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5b31e5d4-4198-4192-9a1e-168839e6d15b · outbound

This paper cites Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:25.755564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7640d3db-eb56-42a2-8184-df80dc41d9c6 · outbound

This paper cites Serious Game.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Serious Game

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:36.841318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6befa69f-ec8b-41a4-b925-6d717e604b90 · outbound

This paper cites Personalized Dynamic Difficulty Adjustment Imitation Learning Meets Reinforcement Learning,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Personalized Dynamic Difficulty Adjustment Imitation Learning Meets Reinforcement Learning,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:26.379098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6cb2ba1-df6d-489e-9314-cdc4fa30bcc0 · outbound

This paper cites Expanding Expressive Range: Evaluation Methodologies for Procedural Content Generation,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Expanding Expressive Range: Evaluation Methodologies for Procedural Content Generation,

Reference 34

Resolution: verified exact; doi, observed 2026-08-07T14:57:30.382483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation af8c9295-d17c-450b-8b36-14d352f926a6 · outbound

This paper cites Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:26.695564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf0bb1c3-8702-481b-ad8f-36e70ee77f13 · outbound

This paper cites Beyond Playing to Win: Creating a Team of Agents With Distinct Behaviors for Automated Gameplay,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Beyond Playing to Win: Creating a Team of Agents With Distinct Behaviors for Automated Gameplay,

Reference 36

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:57:32.054694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 172d2b11-d35d-4105-ac7c-8e1810b8d199 · outbound

This paper cites Automated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Automated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics,

Reference 37

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:57:32.599542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6288a8e2-fd98-4aa4-8a6f-2fb1003055b1 · outbound

This paper cites Automated Play-Testing through RL Based Human-Like Play-Styles Generation,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Automated Play-Testing through RL Based Human-Like Play-Styles Generation,

Reference 38

Resolution: verified exact; doi, observed 2026-08-07T14:57:30.036275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 012e44ae-4e85-4cf6-bd33-68d3903bfd27 · outbound

This paper cites A data-driven procedural-content-generation approach for educational games,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents A data-driven procedural-content-generation approach for educational games,

Reference 39

Resolution: verified exact; doi, observed 2026-08-07T14:57:29.740945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 92c8c24b-8a85-4d73-aec2-a4c327d25fb5 · outbound

This paper cites Procedural content generation based on a genetic algorithm in a serious game for obstructive sleep apnea,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Procedural content generation based on a genetic algorithm in a serious game for obstructive sleep apnea,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:28.470399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5e06525-299f-4239-84f9-13616d64eede · outbound

This paper cites General Video Game AI: Competition, Challenges and Opportunities,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents General Video Game AI: Competition, Challenges and Opportunities,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:27.476070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10354d1e-deb6-4cd7-aaf7-2292a6e6612c · outbound

This paper cites learn” and “predict.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents learn” and “predict

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:36.362186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae9c9fbf-e56c-4dd5-84a7-04e00f9b70fd · outbound

This paper cites General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms,

Reference 43

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:57:32.374979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f4c0d494-4c63-4e27-b7c6-8bc972250af0 · outbound

This paper cites MAP-Elites to Generate a Team of Agents that Elicits Diverse Automated Gameplay,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents MAP-Elites to Generate a Team of Agents that Elicits Diverse Automated Gameplay,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:27.814161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ac3c171-12b4-41b3-baf3-3b012ad3235c · outbound

This paper cites Artificial Players in the Design Process: Developing an Automated Testing Tool for Game Level and World Design,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Artificial Players in the Design Process: Developing an Automated Testing Tool for Game Level and World Design,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:28.060259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7abd787-c7ab-48fb-b174-74af976642a0 · outbound

This paper cites Automated game testing using computer vision methods,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Automated game testing using computer vision methods,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:28.207510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61abb114-50d6-49b3-9570-5db9a82c73f3 · outbound

This paper cites Game Description,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Game Description,

Reference 47

Resolution: verified exact; doi, observed 2026-08-07T14:57:29.457387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 393bbd4b-bb13-4f75-badb-7e486d1cb39a · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:28.632716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57bc8846-1f87-426f-b9c9-77d0ed80ebc9 · outbound

This paper cites an unresolved cited work.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:57:34.416550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 34723bf1-74bb-409f-827c-a737b3e21090 · outbound

This paper cites Creating Competitive Opponents for Serious Games through Dynamic Difficulty Adjustment,.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Creating Competitive Opponents for Serious Games through Dynamic Difficulty Adjustment,

Reference 51

Resolution: verified exact; doi, observed 2026-08-07T14:57:29.189553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b8fe0360-aef8-4c96-a4e5-12b91b5c5335 · outbound

This paper cites an unresolved cited work.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Unresolved cited work

Reference 2022

Resolution: verified exact; doi, observed 2026-08-07T14:57:31.054973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf6499d2-b35b-49c2-b671-034fc6aa57af · outbound

This paper cites Available: https://proceedings.neurips.cc/paper/2021/hash/d58e2f077670f4de9cd7963c857f2534-Abstract.html.

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents Available: https://proceedings.neurips.cc/paper/2021/hash/d58e2f077670f4de9cd7963c857f2534-Abstract.html

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:57:34.694793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers