Hi,
I want to do some reinforcement learning in Julia, but when I want to perform my action, it always gives me this error. do you know what i should do? I already downloaded the latest version of the packages.
function action(s_norm)
println("greedy Action")
act_values = model(s_norm |> gpu)
a = Flux.onecold(act_values)
end
the “model” is my neural network.
greedy Action
ERROR: UndefVarError: parameters not defined
Stacktrace:
[1] classify_arguments(job::GPUCompiler.CompilerJob, codegen_f::LLVM.Function)
@ GPUCompiler C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\irgen.jl:312
[2] lower_byval(job::GPUCompiler.CompilerJob, mod::LLVM.Module, entry_f::LLVM.Function)
@ GPUCompiler C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\irgen.jl:354
[3] process_entry!(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#16", Tuple{CUDA.CuKernelContext, CuDeviceVector{Float32, 1}, Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(relu), Tuple{Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Nothing, typeof(+), Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}, Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}}}}}, Int64}}}, mod::LLVM.Module, entry::LLVM.Function)
@ GPUCompiler C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\ptx.jl:84
[4] irgen(job::GPUCompiler.CompilerJob, method_instance::Core.MethodInstance, world::UInt64)
@ GPUCompiler C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\irgen.jl:62
[5] macro expansion
@ C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\driver.jl:130 [inlined]
[6] macro expansion
@ C:\Users\aetan.julia\packages\TimerOutputs\LDL7n\src\TimerOutput.jl:252 [inlined]
[7] macro expansion
@ C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\driver.jl:129 [inlined]
[8] emit_llvm(job::GPUCompiler.CompilerJob, method_instance::Any, world::UInt64; libraries::Bool, deferred_codegen::Bool, optimize::Bool, only_entry::Bool)
@ GPUCompiler C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\utils.jl:62
[9] emit_llvm
@ C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\utils.jl:60 [inlined]
[10] cufunction_compile(job::GPUCompiler.CompilerJob)
@ CUDA C:\Users\aetan.julia\packages\CUDA\M4jkK\src\compiler\execution.jl:305
[11] check_cache
@ C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\cache.jl:44 [inlined]
[12] cached_compilation
@ C:\Users\aetan.julia\packages\GPUArrays\Z5nPF\src\host\broadcast.jl:57 [inlined]
[13] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#16", Tuple{CUDA.CuKernelContext, CuDeviceVector{Float32, 1}, Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(relu), Tuple{Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Nothing, typeof(+), Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}, Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}}}}}, Int64}}}, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
@ GPUCompiler C:\Users\aetan.julia\packages\GPUCompiler\XwWPj\src\cache.jl:0
[14] cufunction(f::GPUArrays.var"#broadcast_kernel#16", tt::Type{Tuple{CUDA.CuKernelContext, CuDeviceVector{Float32, 1}, Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(relu), Tuple{Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Nothing, typeof(+), Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}, Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}}}}}, Int64}}; name::Nothing, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ CUDA C:\Users\aetan.julia\packages\CUDA\M4jkK\src\compiler\execution.jl:294
[15] cufunction
@ C:\Users\aetan.julia\packages\CUDA\M4jkK\src\compiler\execution.jl:288 [inlined]
[16] macro expansion
@ C:\Users\aetan.julia\packages\CUDA\M4jkK\src\compiler\execution.jl:102 [inlined]
[17] #launch_heuristic#280
@ C:\Users\aetan.julia\packages\CUDA\M4jkK\src\gpuarrays.jl:17 [inlined]
[18] launch_heuristic
@ C:\Users\aetan.julia\packages\CUDA\M4jkK\src\gpuarrays.jl:17 [inlined]
[19] copyto!
@ C:\Users\aetan.julia\packages\GPUArrays\Z5nPF\src\host\broadcast.jl:63 [inlined]
[20] copyto!
@ .\broadcast.jl:936 [inlined]
[21] copy
@ C:\Users\aetan.julia\packages\GPUArrays\Z5nPF\src\host\broadcast.jl:47 [inlined]
[22] materialize
@ .\broadcast.jl:883 [inlined]
[23] (::Dense{typeof(relu), CuArray{Float32, 2}, CuArray{Float32, 1}})(x::CuArray{Float32, 1})
@ Flux C:\Users\aetan.julia\packages\Flux\qp1gc\src\layers\basic.jl:147
[24] applychain
@ C:\Users\aetan.julia\packages\Flux\qp1gc\src\layers\basic.jl:36 [inlined]
[25] (::Chain{Tuple{Dense{typeof(relu), CuArray{Float32, 2}, CuArray{Float32, 1}}, Dense{typeof(relu), CuArray{Float32, 2}, CuArray{Float32, 1}}, Dense{typeof(tanh), CuArray{Float32, 2}, CuArray{Float32, 1}}}})(x::CuArray{Float32, 1})
@ Flux C:\Users\aetan.julia\packages\Flux\qp1gc\src\layers\basic.jl:38
[26] action(s_norm::Vector{Float32})
@ Main c:\Users\aetan\Desktop\DQN_Shems\DQN_Test.jl:58
[27] episode!(env::Shems{Reinforce.ShemsEnv_DQN.ShemsState{Float32}}; NUM_STEPS::Int64, train::Bool, render::Bool, track::Int64, rng_ep::Int64)
@ Main c:\Users\aetan\Desktop\DQN_Shems\DQN_Test.jl:75
[28] top-level scope
@ c:\Users\aetan\Desktop\DQN_Shems\DQN_Reinforce_Shems.jl:67
