Pith. Sign in

RECORD

Alpha-Mini: Minichess Agent with Deep Reinforcement Learning

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Michael Sun, Robert Tan

Machine Learning · Submitted 22 Dec 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The final agent achieves a near (.97) perfect win rate against a random agent.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2112.13666 (version 1)
Pith ID4VLVTLHT, Pith's permanent record of this paper and its review
CitationMichael Sun, Robert Tan. "Alpha-Mini: Minichess Agent with Deep Reinforcement Learning." arXiv:2112.13666 (2021). https://pith.science/paper/4VLVTLHT
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,822 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.09 · human participants 0.02 · controlled comparison 0.85 · randomized assignment 0.03 · states quantitative result 0.98 · compares with prior results 0.73 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We train an agent to compete in the game of Gardner minichess, a downsized variation of chess played on a 5x5 board. We motivated and applied a SOTA actor-critic method Proximal Policy Optimization with Generalized Advantage Estimation. Our initial task centered around training the agent against a random agent. Once we obtained reasonable performance, we then adopted a version of iterative policy improvement adopted by AlphaGo to pit the agent against increasingly stronger versions of itself, and evaluate the resulting performance gain. The final agent achieves a near (.97) perfect win rate against a random agent. We also explore the effects of pretraining the network using a collection of positions obtained via self-play.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We train an agent to compete in the game of Gardner minichess, a downsized variation of chess played on a 5x5 board. contribution 0.56 0.86 0.03 asserted 0.97
s1 We motivated and applied a SOTA actor-critic method Proximal Policy Optimization with Generalized Advantage Estimation. method 0.99 0.89 0.03 asserted 0.99
s2 Our initial task centered around training the agent against a random agent. method 0.99 0.95 0.02 asserted 0.98
s3 Once we obtained reasonable performance, we then adopted a version of iterative policy improvement adopted by AlphaGo to pit the agent against increasingly stronger versions of itself, and evaluate the resulting performance gain. method 0.99 0.94 0.07 asserted 0.96
s4 The final agent achieves a near (.97) perfect win rate against a random agent. result 1.00 0.96 0.43 asserted 1.00
s5 We also explore the effects of pretraining the network using a collection of positions obtained via self-play. method 0.65 0.91 0.04 asserted 0.74

What kind of paper the classifier reports this is: empirical study 0.63 · evidence from observation or experiment 0.54.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We train an agent to compete in the game of Gardner minichess, a downsized variation of chess played on a 5x5 board. We motivated and applied a SOTA actor-critic method Proximal Policy Optimization with Generalized Advantage Estimation. Our initial task centered around training the agent against a random agent. Once we obtained reasonable performance, we then adopted a version of iterative policy improvement adopted by AlphaGo to pit the agent against increasingly stronger versions of itself, and evaluate the resulting performance gain. The final agent achieves a near (.97) perfect win rate against a random agent. We also explore the effects of pretraining the network using a collection of positions obtained via self-play.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.