Merge pull request #10 from biaslab/experiments

update Experiments

Author: wouterwln

Makefile

@@ -2,14 +2,14 @@
 
 # Detect number of CPU cores and use (cores - 2) for Julia threads, minimum 1
 NPROC := $(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
-JULIA_THREADS := $(shell expr $(NPROC) - 2 || echo 1)
+JULIA_THREADS := $(shell expr $(NPROC) - 1 || echo 1)
 # Ensure minimum of 1 thread
 ifeq ($(shell expr $(JULIA_THREADS) \< 1), 1)
     JULIA_THREADS := 1
 endif
 
 # Define experiment parameters
-MINIGRID_PARAMS := --save-results --parallel --save-video --n-iterations 40 --n-episodes 200 --time-horizon 25 --grid-size 4
+MINIGRID_PARAMS := --save-results --parallel --save-video --n-iterations 50 --n-episodes 200 --time-horizon 25 --grid-size 4
 DEBUG_MINIGRID_PARAMS := --grid-size 4 --time-horizon 25 --save-frame --iterations 40 --save-animation
 STOCHASTIC_MAZE_PARAMS := -r --save-results
 DEBUG_STOCHASTIC_MAZE_PARAMS := --save-frame --iterations 50

src/models/minigrid/efe.jl

@@ -54,7 +54,7 @@ import RxInfer: Categorical
         key_door_state_observation_marginalcomponents = JointMarginalMetaComponent[]
 
         for x in 1:7, y in 1:7
-            marginalstorage = JointMarginalStorage(Contingency(ones(number_type, size(observation_tensors[x, y]))))
+            marginalstorage = JointMarginalStorage(Contingency(collect(observation_tensors[x, y])))
             location_observation_marginalcomponent = JointMarginalMetaComponent(marginalstorage, 1, 2)
             push!(location_observation_marginalcomponents, location_observation_marginalcomponent)
             orientation_observation_marginalcomponent = JointMarginalMetaComponent(marginalstorage, 1, 3)
@@ -66,14 +66,14 @@ import RxInfer: Categorical
             key_door_state_observation_marginalcomponent = JointMarginalMetaComponent(marginalstorage, 1, 6)
             push!(key_door_state_observation_marginalcomponents, key_door_state_observation_marginalcomponent)
             decomposed_tensor = observation_tensors[x, y]
-            future_observations[x, y] ~ DiscreteTransition(current_location, decomposed_tensor, current_orientation, key_location, door_location, current_key_door_state) where {meta=marginalstorage}
-            future_observations[x, y] ~ Categorical(fill(number_type(1 / 5), 5))
+            # future_observations[x, y] ~ DiscreteTransition(current_location, decomposed_tensor, current_orientation, key_location, door_location, current_key_door_state) where {meta=marginalstorage}
+            # future_observations[x, y] ~ Categorical(fill(number_type(1 / 5), 5))
         end
-        location[t] ~ Ambiguity(observations[1, 1]) where {meta=JointMarginalMeta(location_observation_marginalcomponents)}
-        orientation[t] ~ Ambiguity(observations[1, 1]) where {meta=JointMarginalMeta(orientation_observation_marginalcomponents)}
-        key_location ~ Ambiguity(observations[1, 1]) where {meta=JointMarginalMeta(key_location_observation_marginalcomponents)}
-        door_location ~ Ambiguity(observations[1, 1]) where {meta=JointMarginalMeta(door_location_observation_marginalcomponents)}
-        key_door_state[t] ~ Ambiguity(observations[1, 1]) where {meta=JointMarginalMeta(key_door_state_observation_marginalcomponents)}
+        location[t] ~ Ambiguity(1) where {meta=JointMarginalMeta(location_observation_marginalcomponents)}
+        orientation[t] ~ Ambiguity(1) where {meta=JointMarginalMeta(orientation_observation_marginalcomponents)}
+        key_location ~ Ambiguity(1) where {meta=JointMarginalMeta(key_location_observation_marginalcomponents)}
+        door_location ~ Ambiguity(1) where {meta=JointMarginalMeta(door_location_observation_marginalcomponents)}
+        key_door_state[t] ~ Ambiguity(1) where {meta=JointMarginalMeta(key_door_state_observation_marginalcomponents)}
     end
     location[end] ~ goal
     orientation[end] ~ Categorical(fill(number_type(1 / 4), 4))

src/models/minigrid/klcontrol.jl

@@ -26,10 +26,14 @@ import RxInfer: Categorical
     end
     current_orientation ~ DiscreteTransition(orientation_observation, diageye(number_type, 4))
 
+    current_location_ow ~ OneWay(current_location)
+    current_orientation_ow ~ OneWay(current_orientation)
+    current_key_door_state_ow ~ OneWay(current_key_door_state)
+
     # Planning (Active Inference)
-    previous_location = current_location
-    previous_orientation = current_orientation
-    previous_key_door_state = current_key_door_state
+    previous_location = current_location_ow
+    previous_orientation = current_orientation_ow
+    previous_key_door_state = current_key_door_state_ow
     for t in 1:T
         u[t] ~ Categorical(fill(number_type(1 / 5), 5))
         location[t] ~ DiscreteTransition(previous_location, location_transition_tensor, previous_orientation, key_location, door_location, previous_key_door_state, u[t])
@@ -58,6 +62,10 @@ RxInfer.GraphPPL.default_constraints(::typeof(klcontrol_minigrid_agent)) = klcon
     μ(current_orientation) = p_current_orientation
     μ(current_key_door_state) = p_current_key_door_state
 
+    μ(current_location_ow) = p_current_location
+    μ(current_orientation_ow) = p_current_orientation
+    μ(current_key_door_state_ow) = p_current_key_door_state
+
     μ(location) = p_future_locations
     μ(orientation) = p_future_orientations
     μ(key_door_state) = p_future_key_door_states

src/rules/full_tensor/uncached.jl

@@ -185,7 +185,7 @@ end
 end
 
 # Rules for observation model for future observations (m_out is categorical)
-@rule DiscreteTransition(:out, Marginalisation) (m_in::Categorical, m_T1::Categorical, m_T2::Categorical, m_T3::Categorical, m_T4::Categorical, q_a::PointMass{<:AbstractArray{T,7}}, meta::Any) where {T} = begin
+@rule DiscreteTransition(:out, Marginalisation) (m_in::Categorical, m_T1::Categorical, m_T2::Categorical, m_T3::Categorical, m_T4::Categorical, q_a::PointMass{<:AbstractArray{T,6}}, meta::Any) where {T} = begin
     eloga = mean(q_a)
     size_result = size(eloga)[1]
     return Categorical(fill(T(1 / size_result), size_result); check_args=false)