A toolkit for auto-generation of OpenAI Gym environments from RDDL description files.
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Updated
Aug 2, 2024 - Python
A toolkit for auto-generation of OpenAI Gym environments from RDDL description files.
Planning through backpropagation using TensorFlow.
A toolkit for working with RDDL domains in Python3.
Tensorflow is not only an well designed deep learning toolbox, but also a standard symbolic programming framework. In this repository, we show how to use tensorflow to do classical planning task on deterministic, continous action, continous space problems.
Probabilistic planning in continuous state-action MDPs in TensorFlow.
Planning using Reinforcement Learning
Hosts domain and instance RDDL files, covering problems from a wide range of disciplines, integration with the pyRDDLGym ecosystem.
RDDL syntax highlighting for Visual Studio Code
Symbolic compilation of RDDL domains, Dynamic Bayes net (DBN) visualization, symbolic dynamic programming (SDP).
JAX compilation of RDDL description files, and a differentiable planner in JAX.
Gurobi compilation of RDDL description files to mixed-integer programs, and optimization tools.
Docker files for connecting the PROST planner with pyRDDLGym.
Wrappers for reinforcement learning algorithms (i.e. stable baselines 3, RLlib) to work with pyRDDLGym.
Graphical integrated development environment for RDDL.
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