REVIEW 3 cited by
RLCard: A Toolkit for Reinforcement Learning in Card Games
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. In this paper, we provide an overview of the key components in RLCard, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments. The codes and documents are available at https://github.com/datamllab/rlcard
Forward citations
Cited by 3 Pith papers
-
LLMs as Agentic Cooperative Players in Multiplayer UNO
LLMs can beat random agents in UNO, but as cooperative partners only LLaMA3.3-70B with cloze prompting gave a significant teammate win-rate gain (35.00% to 35.96%).
-
Cardiverse: Harnessing LLMs for Novel Card Game Prototyping
An LLM-based pipeline generates novel card game variants, validates their code using gameplay records, and builds competitive AI agents from ensembles of LLM-written scoring functions.
-
Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker
In a 100,000-game comparison, both DQN and CFR agents raised with weak hands (bluffed) at different rates but with roughly similar success rates.
Discussion (0). Continue with ORCID to comment.