activity
20192022
most citedDiscrete and Continuous Action Representation for Practical RL in Video Games

39 citations · 46 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20226 cited

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas +19

Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more…

cs.LG20201 cited

Reinforcement Learning Agents for Ubisoft's Roller Champions

Nancy Iskander, Aurelien Simoni, Eloi Alonso +1

In recent years, Reinforcement Learning (RL) has seen increasing popularity in research and popular culture. However, skepticism still surrounds the practicality of RL in modern vi…

cs.LG2020

Deep Reinforcement Learning for Navigation in AAA Video Games

Eloi Alonso, Maxim Peter, David Goumard +1

In video games, non-player characters (NPCs) are used to enhance the players' experience in a variety of ways, e.g., as enemies, allies, or innocent bystanders. A crucial component…

cs.LG201939 cited

Discrete and Continuous Action Representation for Practical RL in Video Games

Olivier Delalleau, Maxim Peter, Eloi Alonso +1

While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like t…

cs.CV2019

Adversarial Generation of Handwritten Text Images Conditioned on Sequences

Eloi Alonso, Bastien Moysset, Ronaldo Messina

State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partiall…