jjschwartz Goto Github PK
Name: Jonathon Schwartz
Type: User
Bio: PhD student at the Australian National University working on planning and reinforcement learning in partially observable, multi-agent environments.
Jonathon Schwartz's Projects
ba-posgmcp
Bayes Adaptive Monte-Carlo Planning algorithm for Partially Observable Stochastic Games
emacs-from-scratch
An example of a fully custom Emacs configuration developed live on YouTube!
highway-env
A minimalist environment for decision-making in autonomous driving
me-site
Repo for personal website content
minidrl
Minimal implementations of distributed, recurrent, deep reinforcement learning algorithms
networkattacksimulator
An environment for testing AI pentesting agents against a simulated network.
open_spiel
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
oppt
On-line POMDP Planning Toolkit
pomcp-for-beginners
An implementation of the POMCP algorithm in python in the hopes of learning something new.
posggym-agents
Agents for POSGGym environments
potmmcp
Implementation of the Partially Observable Type-based Meta Monte-Carlo Planning (POTMMCP) algorithm and experiments.
ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
rl-an-intro
Implementations of the different Reinforcement Learning problems and agents covered in Richard Sutton and Andrew Barto's book "Reinforcement Learning: An Introduction".
rlalgs
Implementations of RL algorithms using tensorflow based off of OpenAI spinningup tutorials
rltorch
Deep RL algorithm implementations using pytorch
sarsop
Efficient Point-Based POMDP Planning by Approximating
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