add TabularLearner

src/components/agents/dyna_agent.jl

@@ -65,7 +65,7 @@ end
 RLBase.update!(model::AbstractEnvironmentModel, t::AbstractTrajectory, π::AbstractPolicy) =
     update!(model, t)
 
-function RLBase.update!(model::AbstractEnvironmentModel, buffer::AbstractTrajectory)
-    transitions = extract_experience(buffer, model)
-    isnothing(transitions) || update!(model, transitions)
-end
+# function RLBase.update!(model::AbstractEnvironmentModel, buffer::AbstractTrajectory)
+#     transitions = extract_experience(buffer, model)
+#     isnothing(transitions) || update!(model, transitions)
+# end

src/components/learners/abstract_learner.jl

@@ -0,0 +1,21 @@
+export AbstractLearner
+
+using Flux
+
+"""
+    (learner::AbstractLearner)(env)
+
+A learner is usually used to estimate state values, state-action values or distributional values based on experiences.
+"""
+abstract type AbstractLearner end
+
+function (learner::AbstractLearner)(env) end
+
+"""
+    get_priority(p::AbstractLearner, experience)
+"""
+function RLBase.get_priority(p::AbstractLearner, experience) end
+
+# TODO: deprecate this default function
+Flux.testmode!(learner::AbstractLearner, mode = true) =
+    Flux.testmode!(learner.approximator, mode)

src/components/learners/learners.jl

@@ -1,20 +1,2 @@
-export AbstractLearner, extract_experience
-
-using Flux
-
-"""
-    (learner::AbstractLearner)(env)
-
-A learner is usually used to estimate state values, state-action values or distributional values based on experiences.
-"""
-abstract type AbstractLearner end
-
-function (learner::AbstractLearner)(env) end
-
-"""
-    get_priority(p::AbstractLearner, experience)
-"""
-function RLBase.get_priority(p::AbstractLearner, experience) end
-
-Flux.testmode!(learner::AbstractLearner, mode = true) =
-    Flux.testmode!(learner.approximator, mode)
+include("abstract_learner.jl")
+include("tabular_learner.jl")

src/components/learners/tabular_learner.jl

@@ -0,0 +1,34 @@
+export TabularLearner
+
+"""
+    TabularLearner{S, T}
+
+Use a `Dict{S,Vector{T}}` to store action probabilities.
+"""
+struct TabularLearner{S,T} <: AbstractPolicy
+    table::Dict{S,Vector{T}}
+end
+
+TabularLearner() = TabularLearner{Int,Float32}()
+TabularLearner{S}() = TabularLearner{S,Float32}()
+TabularLearner{S,T}() = TabularLearner(Dict{S,Vector{T}}())
+
+function (p::TabularLearner)(env::AbstractEnv)
+    s = get_state(env)
+    if haskey(p.table, s)
+        p.table[s]
+    elseif ActionStyle(env) === FULL_ACTION_SET
+        mask = get_legal_actions_mask(env)
+        prob = mask ./ sum(mask)
+        p.table[s] = prob
+        prob
+    elseif ActionStyle(env) === MINIMAL_ACTION_SET
+        n = length(get_actions(env))
+        prob = fill(1 / n, n)
+        p.table[s] = prob
+        prob
+    end
+end
+
+update!(p::TabularLearner, experience::Pair) = p.table[first(experience)] = last(experience)
+