diff --git a/Project.toml b/Project.toml
index e9e8962..5b21751 100644
--- a/Project.toml
+++ b/Project.toml
@@ -31,8 +31,8 @@ ColorSchemes = "3"
 Distributions = "0.25"
 Flux = "0.14"
 Images = "0.25, 0.26"
-POMDPTools = "0.1"
-POMDPs = "0.9"
+POMDPTools = "1"
+POMDPs = "1"
 Parameters = "0.12"
 Plots = "1"
 Reexport = "1"
@@ -43,4 +43,4 @@ ValueHistories = "0.5"
 WeightsAndBiasLogger = "0.5"
 Zygote = "0.6"
 cuDNN = "1"
-julia = "1.6 - 1.10"
+julia = "1.6 - 1.11"
diff --git a/src/extras/spectral_normalization.jl b/src/extras/spectral_normalization.jl
index e1acab7..7526612 100644
--- a/src/extras/spectral_normalization.jl
+++ b/src/extras/spectral_normalization.jl
@@ -1,13 +1,13 @@
 # Power iteration algorithm for computing the spectral norm
 function power_iteration!(W, u, n_iterations)
-    v = nothing
-    for i=1:n_iterations
-        Wu = W' * u
-        v = Wu ./ (norm(Wu) + eps(Float32))
-        Wv = W * v
-        u .= Wv ./ (norm(Wv) + eps(Float32))
-    end
-    u, v
+  v = nothing
+  for i=1:n_iterations
+      Wu = W' * u
+      v = Wu ./ (norm(Wu) + eps(Float32))
+      Wv = W * v
+      u .= Wv ./ (norm(Wv) + eps(Float32))
+  end
+  u, v
 end
 
 # Compute the maximum singular value
@@ -62,7 +62,7 @@ struct ConvSN{N,M,F,A,V, I<:Int, VV<:AbstractArray}
   u::VV # Left vector for power iteration
 end
 
-function ConvSN(w::AbstractArray{T,N}, b::Union{Flux.Zeros, AbstractVector{T}}, σ = identity;
+function ConvSN(w::AbstractArray{T,N}, b::Union{Bool, AbstractVector{T}}, σ = identity;
               stride = 1, pad = 0, dilation = 1, n_iterations = 1) where {T,N}
   stride = Flux.expand(Val(N-2), stride)
   dilation = Flux.expand(Val(N-2), dilation)
@@ -73,7 +73,7 @@ end
 
 function ConvSN(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
             init = Flux.glorot_uniform,  stride = 1, pad = 0, dilation = 1,
-            weight = Flux.convfilter(k, ch, init = init), bias = Flux.zeros(ch[2]), n_iterations = 1) where N
+            weight = Flux.convfilter(k, ch, init = init), bias = false, n_iterations = 1) where N
   ConvSN(weight, bias, σ, stride = stride, pad = pad, dilation = dilation, n_iterations = n_iterations)
 end