Winning at Pokémon Random Battles Using Reinforcement Learning

Pokémon battling is a challenging domain for reinforcement learning techniques, due to the massive state space, stochasticity, and partial observability. We demonstrate an agent which employs a Monte Carlo Tree Search informed by a actor-critic network trained using Proximal Policy Optimization with experience collected through self-play. The agent peaked at rank 8 (1693 Elo) on the official Pokémon Showdown gen4randombattles ladder, which is the best known performance by any non-human agent for this format. This strong showing lays the foundation for superhuman performance in Pokémon and other complex turn-based games of imperfect information, expanding the viability of methods which have historically been used in perfect-information games.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Wang, Jett

Advisor dc:contributor.advisor

• Tenenbaum, Joshua

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153888

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153888