Added minimizing reward

src/RL/LocalCOMEnv.jl

@@ -159,19 +159,6 @@ function state_update_hook!(env_helper::LocalCOMEnvHelper, particles::Vector{Par
     return nothing
 end
 
-"""
-    minimizing_reward(value::Float64, max_value::Float64)
-
-Returns the reward such that it is 0 for value=max_value and 1 for value=0.
-"""
-function minimizing_reward(value::Float64, max_value::Float64)
-    if value > max_value
-        error("value > max_value")
-    end
-
-    return ((max_value - value) / (max_value + value))^2
-end
-
 function update_reward!(env::LocalCOMEnv, env_helper::LocalCOMEnvHelper, particle::Particle)
     id = particle.id
 

src/RL/RL.jl

@@ -5,7 +5,7 @@ export run_rl, LocalCOMEnv
 using Base: OneTo
 
 using ReinforcementLearning
-using Flux: InvDecay
+using Flux: Flux
 using Intervals
 using StaticArrays: SVector
 using LoopVectorization: @turbo
@@ -22,6 +22,7 @@ include("EnvHelper.jl")
 
 include("States.jl")
 include("Hooks.jl")
+include("Reward.jl")
 
 function gen_agent(n_states::Int64, n_actions::Int64, ϵ_stable::Float64)
     # TODO: Optimize warmup and decay
@@ -31,8 +32,9 @@ function gen_agent(n_states::Int64, n_actions::Int64, ϵ_stable::Float64)
     policy = QBasedPolicy(;
         learner=MonteCarloLearner(;
             approximator=TabularQApproximator(;
-                n_state=n_states, n_action=n_actions, opt=InvDecay(1.0)
+                n_state=n_states, n_action=n_actions, opt=Flux.InvDecay(1.0)
             ),
+            γ=0.95, # Reward discount
         ),
         explorer=EpsilonGreedyExplorer(;
             kind=:linear,

src/RL/Reward.jl

@@ -0,0 +1,3 @@
+function minimizing_reward(value::Float64, max_value::Float64)
+    return exp(-0.5 * (value / (max_value / 3))^2)
+end