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Adaptive importance sampling modification to MPPI

License: MIT License

Julia 98.65% Python 1.35%
nonlinear-control model-predictive-control mppi optimal-control mpc sampling-based-planning sampling-based-control

mpopis's Issues

Question about the definition of reward()

Hello, I have a question about the reward() function here:

MPOPIS/src/mppi_mpopi_policies.jl

Line 174 in ceff32b

trajectory_cost = trajectory_cost - reward(env) + control_costs

I can only find that it's in CommonRLInterface, is there any implementation of reward() in this MPPI code base? And how is the reward() function here related to the pseudocode in the paper?
Thank you!

No Compat entries

No compat entires in the Project.toml causing issues with compatibility as dependencies are updated.

Package precompile failed

After I installed the package, it shows that the precompile of Conda, Pycall and MPOPIS failed, then I precompiled it again and got this:
precompile_fail
To solve the problem, I first reinstalled Conda and PyCall:
image
And then reinstalled MPOPIS. PyCall and Conda worked this time, but MPOPIS still failed the precompile:
image
Is there anything wrong? Did I forget anything in the installation?

A question about the mean value of the iterative sampling

Hello!

I have another question about the code here:

MPOPIS/src/mppi_mpopi_policies.jl

Line 465 in ceff32b

pol.U = pol.U + vec(mean(elite, dims=2))

In the code above, the mean value of the sampling is added to pol.U in every iteration, and in the following code, the difference between pol.U and its original value is added to the final sampling result E(LHS):

MPOPIS/src/mppi_mpopi_policies.jl

Line 468 in ceff32b

E = E .+ (pol.U - U_orig)

I think what it means is, the mean value of the final result of the sampling E(LHS) is the sum of the mean value in every iteration.

Can this cause the predictive trajectory calculated from E biases from the sampling in every iteration? Is it reasonable to just use the mean value of the elite trajectory in the last iteration as the mean value of the final sampling result instead of adding all those mean values up?

Thank you for your patience!

No CI

Missing any CI integration including tests and a compact helper.

`Space` is no longer exported by ReinforcementLearningBase from v0.10.0+ 

[ Info: Precompiling MPOPIS [e8a75bc8-90e1-4072-945a-20230e5738f6]
ERROR: LoadError: UndefVarError: `Space` not defined
Stacktrace:
 [1] top-level scope
   @ ~/.julia/packages/MPOPIS/sD3Z4/src/envs/car_racing.jl:28
 [2] include(mod::Module, _path::String)
   @ Base ./Base.jl:457
 [3] include(x::String)
   @ MPOPIS ~/.julia/packages/MPOPIS/sD3Z4/src/MPOPIS.jl:2
 [4] top-level scope
   @ ~/.julia/packages/MPOPIS/sD3Z4/src/MPOPIS.jl:67
 [5] include
   @ ./Base.jl:457 [inlined]
 [6] include_package_for_output(pkg::Base.PkgId, input::String, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::String)
   @ Base ./loading.jl:2045
 [7] top-level scope
   @ stdin:3

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