The slide() function in Active Inference Mountain Car example #329
Description
Thanks for your amazing work about Bayesian Inference! I have learned a lot from the example of 'Active Inference Mountain Car' example. But in the following code:
# We are going to use some private functionality from ReactiveMP,
# in the future we should expose a proper API for this
import RxInfer.ReactiveMP: getrecent, messageout
function create_agent(;T = 20, Fg, Fa, Ff, engine_force_limit, x_target, initial_position, initial_velocity)
Epsilon = fill(huge, 1, 1) # Control prior variance
m_u = Vector{Float64}[ [ 0.0] for k=1:T ] # Set control priors
V_u = Matrix{Float64}[ Epsilon for k=1:T ]
Sigma = 1e-4*diageye(2) # Goal prior variance
m_x = [zeros(2) for k=1:T]
V_x = [huge*diageye(2) for k=1:T]
V_x[end] = Sigma # Set prior to reach goal at t=T
# Set initial brain state prior
m_s_t_min = [initial_position, initial_velocity]
V_s_t_min = tiny * diageye(2)
# Set current inference results
result = nothing
# The `infer` function is the heart of the agent
# It calls the `RxInfer.inference` function to perform Bayesian inference by message passing
compute = (upsilon_t::Float64, y_hat_t::Vector{Float64}) -> begin
m_u[1] = [ upsilon_t ] # Register action with the generative model
V_u[1] = fill(tiny, 1, 1) # Clamp control prior to performed action
m_x[1] = y_hat_t # Register observation with the generative model
V_x[1] = tiny*diageye(2) # Clamp goal prior to observation
data = Dict(:m_u => m_u,
:V_u => V_u,
:m_x => m_x,
:V_x => V_x,
:m_s_t_min => m_s_t_min,
:V_s_t_min => V_s_t_min)
model = mountain_car(T = T, Fg = Fg, Fa = Fa, Ff = Ff, engine_force_limit = engine_force_limit)
result = infer(model = model, data = data)
end
# The `act` function returns the inferred best possible action
act = () -> begin
if result !== nothing
return mode(result.posteriors[:u][2])[1]
else
return 0.0 # Without inference result we return some 'random' action
end
end
# The `future` function returns the inferred future states
future = () -> begin
if result !== nothing
return getindex.(mode.(result.posteriors[:s]), 1)
else
return zeros(T)
end
end
# The `slide` function modifies the `(m_s_t_min, V_s_t_min)` for the next step
# and shifts (or slides) the array of future goals `(m_x, V_x)` and inferred actions `(m_u, V_u)`
slide = () -> begin
model = RxInfer.getmodel(result.model)
(s, ) = RxInfer.getreturnval(model)
varref = RxInfer.getvarref(model, s)
var = RxInfer.getvariable(varref)
slide_msg_idx = 3 # This index is model dependend
(m_s_t_min, V_s_t_min) = mean_cov(getrecent(messageout(var[2], slide_msg_idx)))
m_u = circshift(m_u, -1)
m_u[end] = [0.0]
V_u = circshift(V_u, -1)
V_u[end] = Epsilon
m_x = circshift(m_x, -1)
m_x[end] = x_target
V_x = circshift(V_x, -1)
V_x[end] = Sigma
end
return (compute, act, slide, future)
endI want to know if this line
(m_s_t_min, V_s_t_min) = mean_cov(getrecent(messageout(var[2], slide_msg_idx)))could be replaced by the following line
(m_s_t_min, V_s_t_min) = mean_cov(result.posteriors[:x][2])I have modified the code and get the following result:
Could you give me an explaination?