# Using PPOPolicy with custom environment with action masking in ReinforcementLearning.jl

**URL:** <https://discourse.julialang.org/t/using-ppopolicy-with-custom-environment-with-action-masking-in-reinforcementlearning-jl/69625>\
**Category:** Machine Learning\
**Tags:** question\
**Created:** [October 12, 2021, 2:01pm UTC](https://discourse.julialang.org/t/using-ppopolicy-with-custom-environment-with-action-masking-in-reinforcementlearning-jl/69625 "2021-10-12T14:01:09Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![findmyway](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/findmyway/32/4946_2.png) [@findmyway](https://discourse.julialang.org/u/findmyway)\
**Post date:** [October 12, 2021, 4:17pm UTC](https://discourse.julialang.org/t/using-ppopolicy-with-custom-environment-with-action-masking-in-reinforcementlearning-jl/69625/2 "2021-10-12T16:17:45Z")

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Hi @mfg ,

Could you share the code of initializing the `Trajectory` part?

It should be something like:

```julia
julia> trajectory = MaskedPPOTrajectory(;
                   capacity = UPDATE_FREQ,
                   state = Matrix{Float32} => (ns, N_ENV),
                   action = Vector{Int} => (N_ENV,),
                   legal_actions_mask = Vector{Bool} => (na, N_ENV),
                   action_log_prob = Vector{Float32} => (N_ENV,),
                   reward = Vector{Float32} => (N_ENV,),
                   terminal = Vector{Bool} => (N_ENV,),
               )

```

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