Dev

.github/workflows/CI_full.yml

@@ -30,16 +30,16 @@ jobs:
         arch:
           - x64
     steps:
-      - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@v2
+      - uses: actions/checkout@v6
+      - uses: julia-actions/setup-julia@v3
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: julia-actions/cache@v2
+      - uses: julia-actions/cache@v3
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v5
+      - uses: codecov/codecov-action@v6
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           files: lcov.info

.github/workflows/CI_small.yml

@@ -29,16 +29,16 @@ jobs:
         arch:
           - x64
     steps:
-      - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@v2
+      - uses: actions/checkout@v6
+      - uses: julia-actions/setup-julia@v3
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: julia-actions/cache@v2
+      - uses: julia-actions/cache@v3
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v5
+      - uses: codecov/codecov-action@v6
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           files: lcov.info

.github/workflows/Documenter.yml

@@ -19,8 +19,8 @@ jobs:
     name: Documentation
     runs-on: macOS-latest
     steps:
-      - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@v2
+      - uses: actions/checkout@v6
+      - uses: julia-actions/setup-julia@v3
         with:
           version: '1'
       - uses: julia-actions/julia-buildpkg@v1

README.md

@@ -97,3 +97,9 @@ infer_policies!(aif)
 sample_action!(aif)
 
 ````
+
+## Contributors
+- [@jonathan7773](https://github.com/Jonathan7773) – Developer
+- [@samuelnehrer02](https://github.com/samuelnehrer02) – Developer
+- [@PTWaade](https://github.com/PTWaade) – Developer
+- [@John-Boik](https://github.com/John-Boik) – Developer

src/pomdp/POMDP.jl

@@ -26,10 +26,10 @@ function action_pomdp!(agent::Agent, obs::Vector{Int64})
     action_distribution = Vector{Distributions.Categorical}(undef, n_factors)
 
     #If there was a previous action
-    if !ismissing(agent.states["action"])
+    if !isempty(agent.substruct.states.action)
 
         #Extract it
-        previous_action = agent.states["action"]
+        previous_action = agent.states.action
 
         # If it is not a vector, make it one
         if !(previous_action isa Vector)
@@ -45,19 +45,18 @@ function action_pomdp!(agent::Agent, obs::Vector{Int64})
     infer_states!(agent.substruct, obs)
 
     # If action is empty, update D vectors
-    if ismissing(agent.states["action"]) && !isnothing(agent.substruct.parameters.pD)
-        update_D!(agent.substruct)
+    if isempty(agent.substruct.states.action) && !isnothing(agent.substruct.parameters.pD)
+        update_D(agent.substruct)
     end
 
     # If learning of the B matrix is enabled and agent has a previous action
-    if !ismissing(agent.states["action"]) && !isnothing(agent.substruct.parameters.pB)
-        # Update Transition Matrix
-        update_B!(agent.substruct)
+    if !isempty(agent.substruct.states.action) && !isnothing(agent.substruct.parameters.pB)
+        update_B(agent.substruct)
     end
 
     # If learning of the A matrix is enabled
     if !isnothing(agent.substruct.parameters.pA)
-        update_A!(agent.substruct)
+        update_A(agent.substruct)
     end
 
     # Run policy inference 
@@ -83,18 +82,18 @@ function action_pomdp!(agent::Agent, obs::Tuple{Vararg{Int}})
     obs = collect(obs)
 
     ### Get parameters 
-    alpha = agent.substruct.parameters["alpha"]
-    n_factors = length(agent.substruct.settings["num_controls"])
+    alpha = agent.substruct.parameters.alpha
+    n_factors = length(agent.substruct.settings._n_controls)
 
     # Initialize empty arrays for action distribution per factor
     action_p = Vector{Any}(undef, n_factors)
     action_distribution = Vector{Distributions.Categorical}(undef, n_factors)
 
-     #If there was a previous action
-     if !ismissing(agent.states["action"])
+    #If there was a previous action
+    if !isempty(agent.substruct.states.action)
 
         #Extract it
-        previous_action = agent.states["action"]
+        previous_action = agent.states.action
 
         # If it is not a vector, make it one
         if !(previous_action isa Vector)
@@ -111,24 +110,19 @@ function action_pomdp!(agent::Agent, obs::Tuple{Vararg{Int}})
     infer_states!(agent.substruct, obs)
 
     # If action is empty and pD is not nothing, update D vectors
-    if ismissing(agent.states["action"]) && agent.substruct.pD !== nothing
-        qs_t1 = get_history(agent.substruct)["posterior_states"][1]
-        update_D!(agent.substruct, qs_t1)
+    if isempty(agent.substruct.states.action) && !isnothing(agent.substruct.parameters.pD)
+
+        update_D(agent.substruct)
     end
 
     # If learning of the B matrix is enabled and agent has a previous action
-    if !ismissing(agent.states["action"]) && agent.substruct.pB !== nothing
-
-        # Get the posterior over states from the previous time step
-        states_posterior = get_history(agent.substruct)["posterior_states"][end-1]
-
-        # Update Transition Matrix
-        update_B!(agent.substruct, states_posterior)
+    if !isempty(agent.substruct.states.action) && !isnothing(agent.substruct.parameters.pB)
+        update_B(agent.substruct)
     end
 
     # If learning of the A matrix is enabled
-    if agent.substruct.pA !== nothing
-        update_A!(agent.substruct, obs)
+    if !isnothing(agent.substruct.parameters.pA)
+        update_A(agent.substruct)
     end
 
     # Run policy inference 
@@ -163,26 +157,17 @@ function action_pomdp!(aif::POMDPActiveInference, obs::Vector{Int64})
     infer_states!(aif, obs)
 
     # If action is empty, update D vectors
-    if isempty(get_states(aif, "action")) && !isnothing(aif.parameters.pD)
-        # qs_t1 = get_history(aif, "qs_current")[end]
-        # update_D(aif, qs_t1)
+    if isempty(aif.states.action) && !isnothing(aif.parameters.pD)
         update_D(aif)
     end
 
     # If learning of the B matrix is enabled and agent has a previous action
-    if !isempty(get_states(aif, "action")) && !isnothing(aif.parameters.pB)
-
-        # Get the posterior over states from the previous time step
-        # states_posterior = get_history(aif, "qs_current")[end-1]
-
-        # Update Transition Matrix
-        # update_B(aif, states_posterior)
+    if !isempty(aif.states.action) && !isnothing(aif.parameters.pB)
         update_B(aif)
     end
 
     # If learning of the A matrix is enabled
     if !isnothing(aif.parameters.pA)
-        # update_A(aif, obs)
         update_A(aif)
     end
 
@@ -202,58 +187,6 @@ function action_pomdp!(aif::POMDPActiveInference, obs::Vector{Int64})
     return n_factors == 1 ? action_distribution[1] : action_distribution
 end
 
-# function action_pomdp!(aif::AIF, obs::Vector{Int64})
-
-#     ### Get parameters 
-#     alpha = aif.parameters["alpha"]
-#     n_factors = length(aif.settings["num_controls"])
-
-#     # Initialize empty arrays for action distribution per factor
-#     action_p = Vector{Any}(undef, n_factors)
-#     action_distribution = Vector{Distributions.Categorical}(undef, n_factors)
-
-#     ### Infer states & policies
-
-#     # Run state inference 
-#     infer_states!(aif, obs)
-
-#     # If action is empty, update D vectors
-#     if ismissing(get_states(aif)["action"]) && aif.pD !== nothing
-#         qs_t1 = get_history(aif)["posterior_states"][1]
-#         update_D!(aif, qs_t1)
-#     end
-
-#     # If learning of the B matrix is enabled and agent has a previous action
-#     if !ismissing(get_states(aif)["action"]) && aif.pB !== nothing
-
-#         # Get the posterior over states from the previous time step
-#         states_posterior = get_history(aif)["posterior_states"][end-1]
-
-#         # Update Transition Matrix
-#         update_B!(aif, states_posterior)
-#     end
-
-#     # If learning of the A matrix is enabled
-#     if aif.pA !== nothing
-#         update_A!(aif, obs)
-#     end
-
-#     # Run policy inference 
-#     infer_policies!(aif)
-
-
-#     ### Retrieve log marginal probabilities of actions
-#     log_action_marginals = get_log_action_marginals(aif)
-
-#     ### Pass action marginals through softmax function to get action probabilities
-#     for factor in 1:n_factors
-#         action_p[factor] = softmax(log_action_marginals[factor] * alpha, dims=1)
-#         action_distribution[factor] = Distributions.Categorical(action_p[factor])
-#     end
-
-#     return n_factors == 1 ? action_distribution[1] : action_distribution
-# end
-
 function action_pomdp!(agent::Agent, obs::Int64)
     action_pomdp!(agent::Agent, [obs])
 end