从均匀分布中抽样时没有方法匹配 logpdf

Question

我正在尝试在 julia 中使用强化学习来教一辆不断向后加速（但初始速度为正）的汽车施加制动，以便在向后移动之前尽可能接近目标距离.

为此，我使用了 POMDPs.jl 和 crux.jl，它们有很多求解器（我使用的是 DQN）。我会先列出我认为是脚本的相关部分，然后在最后列出更多内容。

为了定义 MDP，我将制动器的初始位置、速度和力设置为在某些值上均匀分布。

@with_kw struct SliderMDP <: MDP{Array{Float32}, Array{Float32}}
        x0 = Distributions.Uniform(0., 80.)# Distribution to sample initial position
        v0 = Distributions.Uniform(0., 25.) # Distribution to sample initial velocity
        d0 = Distributions.Uniform(0., 2.) # Distribution to sample brake force
        ...
end

我的状态保持 (position, velocity, brake force) 的值，初始状态为：

function POMDPs.initialstate(mdp::SliderMDP)
    ImplicitDistribution((rng) -> Float32.([rand(rng, mdp.x0), rand(rng, mdp.v0), rand(rng, mdp.d0)]))
end

然后，我使用 crux.jl 设置了我的 DQN 求解器并调用了一个函数来求解策略

solver_dqn = DQN(π=Q_network(), S=s, N=30000)
policy_dqn = solve(solver_dqn, mdp)

调用 solve() 给我错误 MethodError: no method matching logpdf(::Distributions.Categorical{Float64, Vector{Float64}}, ::Nothing)。我很确定这是来自初始状态采样，但我不确定为什么或如何修复它。我只是在很短的时间内从各种书籍和在线讲座中学习 RL，因此，对于错误或我设置的模型（或我忘记的任何其他内容）的任何帮助，我们将不胜感激。

更全面的代码：

包：

using POMDPs
using POMDPModelTools
using POMDPPolicies
using POMDPSimulators

using Parameters
using Random

using Crux
using Flux

using Distributions

其余部分：

@with_kw struct SliderMDP <: MDP{Array{Float32}, Array{Float32}}
    x0 = Distributions.Uniform(0., 80.)# Distribution to sample initial position
    v0 = Distributions.Uniform(0., 25.) # Distribution to sample initial velocity
    d0 = Distributions.Uniform(0., 2.) # Distribution to sample brake force
    
    m::Float64 = 1.
    tension::Float64 = 3.
    dmax::Float64 = 2.
    target::Float64 = 80.
    dt::Float64 = .05
    
    γ::Float32 = 1.
    actions::Vector{Float64} = [-.1, 0., .1]
end
    
function POMDPs.gen(env::SliderMDP, s, a, rng::AbstractRNG = Random.GLOBAL_RNG)
    x, ẋ, d = s

    if x >= env.target
        a = .1
    end
    if d+a >= env.dmax || d+a <= 0
        a = 0.
    end
    
    force = (d + env.tension) * -1
    ẍ = force/env.m
    
    # Simulation
    x_ = x + env.dt * ẋ
    ẋ_ = ẋ + env.dt * ẍ
    d_ = d + a

    sp = vcat(x_, ẋ_, d_)
    reward = abs(env.target - x) * -1
        
    return (sp=sp, r=reward)
end

    

function POMDPs.initialstate(mdp::SliderMDP)
    ImplicitDistribution((rng) -> Float32.([rand(rng, mdp.x0), rand(rng, mdp.v0), rand(rng, mdp.d0)]))
end
    
POMDPs.isterminal(mdp::SliderMDP, s) = s[2] <= 0
POMDPs.discount(mdp::SliderMDP) = mdp.γ

mdp = SliderMDP();
s = state_space(mdp); # Using Crux.jl

function Q_network()
    layer1 = Dense(3, 64, relu)
    layer2 = Dense(64, 64, relu)
    layer3 = Dense(64, length(3))
    return DiscreteNetwork(Chain(layer1, layer2, layer3), [-.1, 0, .1])
end

solver_dqn = DQN(π=Q_network(), S=s, N=30000) # Using Crux.jl
policy_dqn = solve(solver_dqn, mdp) # Error comes here

堆栈跟踪：

policy_dqn
MethodError: no method matching logpdf(::Distributions.Categorical{Float64, Vector{Float64}}, ::Nothing)

Closest candidates are:

logpdf(::Distributions.DiscreteNonParametric, !Matched::Real) at C:\Users\name\.julia\packages\Distributions\Xrm9e\src\univariate\discrete\discretenonparametric.jl:106

logpdf(::Distributions.UnivariateDistribution{S} where S<:Distributions.ValueSupport, !Matched::AbstractArray) at deprecated.jl:70

logpdf(!Matched::POMDPPolicies.PlaybackPolicy, ::Any) at C:\Users\name\.julia\packages\POMDPPolicies\wMOK3\src\playback.jl:34

...

logpdf(::Crux.ObjectCategorical, ::Float32)@utils.jl:16
logpdf(::Crux.DistributionPolicy, ::Vector{Float64}, ::Float32)@policies.jl:305
var"#exploration#133"(::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, ::typeof(Crux.exploration), ::Crux.DistributionPolicy, ::Vector{Float64})@policies.jl:302
exploration@policies.jl:297[inlined]
action(::Crux.DistributionPolicy, ::Vector{Float64})@policies.jl:294
var"#exploration#136"(::Crux.DiscreteNetwork, ::Int64, ::typeof(Crux.exploration), ::Crux.MixedPolicy, ::Vector{Float64})@policies.jl:326
var"#step!#173"(::Bool, ::Int64, ::typeof(Crux.step!), ::Dict{Symbol, Array}, ::Int64, ::Crux.Sampler{Main.workspace#2.SliderMDP, Vector{Float32}, Crux.DiscreteNetwork, Crux.ContinuousSpace{Tuple{Int64}}, Crux.DiscreteSpace})@sampler.jl:55
var"#steps!#174"(::Int64, ::Bool, ::Int64, ::Bool, ::Bool, ::Bool, ::typeof(Crux.steps!), ::Crux.Sampler{Main.workspace#2.SliderMDP, Vector{Float32}, Crux.DiscreteNetwork, Crux.ContinuousSpace{Tuple{Int64}}, Crux.DiscreteSpace})@sampler.jl:108
var"#fillto!#177"(::Int64, ::Bool, ::typeof(Crux.fillto!), ::Crux.ExperienceBuffer{Array}, ::Crux.Sampler{Main.workspace#2.SliderMDP, Vector{Float32}, Crux.DiscreteNetwork, Crux.ContinuousSpace{Tuple{Int64}}, Crux.DiscreteSpace}, ::Int64)@sampler.jl:156
solve(::Crux.OffPolicySolver, ::Main.workspace#2.SliderMDP)@off_policy.jl:86
top-level scope@Local: 1[inlined]

Answer 1

简答:

将输出向量更改为 Float32，即 Float32[-.1, 0, .1]。

长答案:

Crux 在网络的输出值上创建一个 Distribution，并且在某些时候 (policies.jl:298) samples a random value from it. It then converts this value to a Float32. Later (utils.jl:15) 它会执行一个 findfirst 以在原始输出数组中找到该值的索引（在分布中存储为 objs），但是因为原始数组仍然是 Float64，所以失败并且 returns 一个 nothing。因此错误。

我相信这（转换采样值而不是 objs 数组 and/or 不使用近似相等检查，即 findfirst(isapprox(x), d.objs)）是包中的一个错误，并且会鼓励你在 Github.

上提出这个问题

从均匀分布中抽样时没有方法匹配 logpdf

no method matching logpdf when sampling from uniform distribution

machine-learning

julia

distribution

reinforcement-learning

markov-decision-process