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[R] Invitation to join an AI Competition: Reconnaissance Blind Chess (NeurIPS 2019) – AI under Uncertainty

[R] Invitation to join an AI Competition: Reconnaissance Blind Chess (NeurIPS 2019) - AI under Uncertainty

We are hosting a fun, online AI competition. Participants create a bot that can play chess, but blind and with the ability to privately sense a 3×3 square of the board each turn! The competition is part of of NeurIPS. Anyone can participate.

$1,000 prize.

Participants do not need to attend the NeurIPS conference and there is no cost.

Play reconnaissance blind chess now.

All are invited to participate in an upcoming computer science competition that is being held as part of the 2019 Conference on Neural Information Processing Systems (NeurIPS, https://nips.cc/), Reconnaissance Blind Chess.

Many of the favorite studied games in artificial intelligence (AI) such as checkers, chess, and Go lack something that is common and critical in real-life decision making, uncertainty.

This is a competition with a simple but powerful twist on what may be considered the most classic game in AI history, chess. Reconnaissance Blind Chess (RBC) is like chess except a player cannot see where her opponent’s pieces are a priori. Rather, she learns partial information about them with the ability to sense a 3×3 square of the board each turn and from the results of moves.

In comparison to poker, which seems to be the most popularly studied game of imperfect information, RBC includes a critical component of long-term planning. Compared to phantom games like Kriegspiel, in RBC players have much more ability to manage their uncertainty, which we believe makes the game more interesting from an AI perspective and more realistic for most scenarios; players are not completely blind, but rather, metaphorically, they simply cannot look everywhere at once.

Participants are welcome to use any code or libraries available.

For more information on the NeurIPS competition, the game itself, or the API, or to play the game to get a feel for it, visit our website below.

All are welcome to create the best RBC bot they can at no cost, and see how well it can play against other bots in the tournament starting on October 21, 2019!

https://rbc.jhuapl.edu

https://i.redd.it/3gthot4i43b31.png

(on twitter: https://twitter.com/ryan_w_gardner/status/1151911206019567617 )

submitted by /u/rwgardner
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[P] fastai-Serving: running containerized inference with fastai models

Code: fastai-serving repo

We’ve been experimenting with some Fast AI models recently for our remote sensing work. Unfortunately, we ran into a lot of issues when trying to deploy those models on large-scale inference jobs (specifically running land-classification on big satellite imagery datasets). This fastai-serving repo is meant to solve this in a way that mimics the TF Serving approach/API. Namely, it helps you package a trained model within a small Docker image (running a mini server) so you can make prediction requests via REST POST requests.

We’re working on expanding the functionality (and are very receptive to any help!). For anyone who’s running inference on large image sets, we usually spin up multiple of these these inference-ready images and run large batch predictions with our open chip-n-scale pipeline.

submitted by /u/wronk17
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[P] Implemented MPPI (Model Predictive Path Integral) in Python with OpenAI Gym pendulum environment (paper: “Information Theoretic MPC for Model-Based Reinforcement Learning”, Williams et al., 2017)

Hi, I have implemented MPPI introduced in the paper “Information Theoretic MPC for Model-Based Reinforcement Learning” (Williams et al., 2017) in Python with the pendulum OpenAI Gym environment. Please feel free to use and improve it!

Repository: https://github.com/ferreirafabio/mppi_pendulum

submitted by /u/whiletrue2
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