add MultiStepSampler

Author: HenriDeh

src/samplers.jl

@@ -1,5 +1,5 @@
 using Random
-export EpisodesSampler, Episode, BatchSampler, NStepBatchSampler, MetaSampler, MultiBatchSampler, DummySampler
+export EpisodesSampler, Episode, BatchSampler, NStepBatchSampler, MetaSampler, MultiBatchSampler, DummySampler, MultiStepSampler
 
 struct SampleGenerator{S,T}
     sampler::S
@@ -29,27 +29,27 @@ StatsBase.sample(::DummySampler, t) = t
 export BatchSampler
 
 struct BatchSampler{names}
-    batch_size::Int
+    batchsize::Int
     rng::Random.AbstractRNG
 end
 
 """
-    BatchSampler{names}(;batch_size, rng=Random.GLOBAL_RNG)
-    BatchSampler{names}(batch_size ;rng=Random.GLOBAL_RNG)
+    BatchSampler{names}(;batchsize, rng=Random.GLOBAL_RNG)
+    BatchSampler{names}(batchsize ;rng=Random.GLOBAL_RNG)
 
-Uniformly sample **ONE** batch of `batch_size` examples for each trace specified
+Uniformly sample **ONE** batch of `batchsize` examples for each trace specified
 in `names`. If `names` is not set, all the traces will be sampled.
 """
-BatchSampler(batch_size; kw...) = BatchSampler(; batch_size=batch_size, kw...)
+BatchSampler(batchsize; kw...) = BatchSampler(; batchsize=batchsize, kw...)
 BatchSampler(; kw...) = BatchSampler{nothing}(; kw...)
-BatchSampler{names}(batch_size; kw...) where {names} = BatchSampler{names}(; batch_size=batch_size, kw...)
-BatchSampler{names}(; batch_size, rng=Random.GLOBAL_RNG) where {names} = BatchSampler{names}(batch_size, rng)
+BatchSampler{names}(batchsize; kw...) where {names} = BatchSampler{names}(; batchsize=batchsize, kw...)
+BatchSampler{names}(; batchsize, rng=Random.GLOBAL_RNG) where {names} = BatchSampler{names}(batchsize, rng)
 
 StatsBase.sample(s::BatchSampler{nothing}, t::AbstractTraces) = StatsBase.sample(s, t, keys(t))
 StatsBase.sample(s::BatchSampler{names}, t::AbstractTraces) where {names} = StatsBase.sample(s, t, names)
 
 function StatsBase.sample(s::BatchSampler, t::AbstractTraces, names, weights = StatsBase.UnitWeights{Int}(length(t)))
-    inds = StatsBase.sample(s.rng, 1:length(t), weights, s.batch_size)
+    inds = StatsBase.sample(s.rng, 1:length(t), weights, s.batchsize)
     NamedTuple{names}(map(x -> collect(t[Val(x)][inds]), names))
 end
 
@@ -75,12 +75,12 @@ function StatsBase.sample(s::BatchSampler, e::EpisodesBuffer{<:Any, <:Any, <:Cir
     p = collect(deepcopy(t.priorities))
     w = StatsBase.FrequencyWeights(p)
     w .*= e.sampleable_inds[1:end-1]
-    inds = StatsBase.sample(s.rng, eachindex(w), w, s.batch_size)
+    inds = StatsBase.sample(s.rng, eachindex(w), w, s.batchsize)
     NamedTuple{(:key, :priority, names...)}((t.keys[inds], p[inds], map(x -> collect(t.traces[Val(x)][inds]), names)...))
 end
 
 function StatsBase.sample(s::BatchSampler, t::CircularPrioritizedTraces, names)
-    inds, priorities = rand(s.rng, t.priorities, s.batch_size)
+    inds, priorities = rand(s.rng, t.priorities, s.batchsize)
     NamedTuple{(:key, :priority, names...)}((t.keys[inds], priorities, map(x -> collect(t.traces[Val(x)][inds]), names)...))
 end
 
@@ -165,41 +165,41 @@ end
 export NStepBatchSampler
 
 """
-    NStepBatchSampler{names}(; n, γ, batch_size=32, stack_size=nothing, rng=Random.GLOBAL_RNG)
+    NStepBatchSampler{names}(; n, γ, batchsize=32, stacksize=nothing, rng=Random.GLOBAL_RNG)
 
 Used to sample a discounted sum of consecutive rewards in the framework of n-step TD learning.
 The "next" element of Multiplexed traces (such as the next_state or the next_action) will be 
 that in up to `n > 1` steps later in the buffer. The reward will be
 the discounted sum of the `n` rewards, with `γ` as the discount factor.
 
-NStepBatchSampler may also be used with n ≥ 1 to sample a "stack" of states if `stack_size` is set 
-to an integer > 1. This samples the (stack_size - 1) previous states. This is useful in the case
-of partial observability, for example when the state is approximated by `stack_size` consecutive 
+NStepBatchSampler may also be used with n ≥ 1 to sample a "stack" of states if `stacksize` is set 
+to an integer > 1. This samples the (stacksize - 1) previous states. This is useful in the case
+of partial observability, for example when the state is approximated by `stacksize` consecutive 
 frames.
 """
-mutable struct NStepBatchSampler{names, S <: Union{Nothing,Int}}
+mutable struct NStepBatchSampler{names, S <: Union{Nothing,Int}, R <: AbstractRNG}
     n::Int # !!! n starts from 1
     γ::Float32
-    batch_size::Int
-    stack_size::S
-    rng::Any
+    batchsize::Int
+    stacksize::S
+    rng::R
 end
 
 NStepBatchSampler(t::AbstractTraces; kw...) = NStepBatchSampler{keys(t)}(; kw...)
-function NStepBatchSampler{names}(; n, γ, batch_size=32, stack_size=nothing, rng=Random.GLOBAL_RNG) where {names} 
+function NStepBatchSampler{names}(; n, γ, batchsize=32, stacksize=nothing, rng=Random.default_rng()) where {names} 
     @assert n >= 1 "n must be ≥ 1."
-    ss = stack_size == 1 ? nothing : stack_size
-    NStepBatchSampler{names, typeof(ss)}(n, γ, batch_size, ss, rng)
+    ss = stacksize == 1 ? nothing : stacksize
+    NStepBatchSampler{names, typeof(ss), typeof(rng)}(n, γ, batchsize, ss, rng)
 end
 
-#return a boolean vector of the valid sample indices given the stack_size and the truncated n for each index.
+#return a boolean vector of the valid sample indices given the stacksize and the truncated n for each index.
 function valid_range(s::NStepBatchSampler, eb::EpisodesBuffer) 
     range = copy(eb.sampleable_inds)
     ns = Vector{Int}(undef, length(eb.sampleable_inds))
-    stack_size = isnothing(s.stack_size) ? 1 : s.stack_size 
+    stacksize = isnothing(s.stacksize) ? 1 : s.stacksize 
     for idx in eachindex(range)
         step_number = eb.step_numbers[idx]
-        range[idx] = step_number >= stack_size && eb.sampleable_inds[idx]
+        range[idx] = step_number >= stacksize && eb.sampleable_inds[idx]
         ns[idx] = min(s.n, eb.episodes_lengths[idx] - step_number + 1)
     end
     return range, ns
@@ -211,19 +211,19 @@ end
 
 function StatsBase.sample(s::NStepBatchSampler, t::EpisodesBuffer, ::Val{names}) where names
     weights, ns = valid_range(s, t)
-    inds = StatsBase.sample(s.rng, 1:length(t), StatsBase.FrequencyWeights(weights[1:end-1]), s.batch_size)
+    inds = StatsBase.sample(s.rng, 1:length(t), StatsBase.FrequencyWeights(weights[1:end-1]), s.batchsize)
     fetch(s, t, Val(names), inds, ns)
 end
 
 function fetch(s::NStepBatchSampler, ts::EpisodesBuffer, ::Val{names}, inds, ns) where names
     NamedTuple{names}(map(name -> collect(fetch(s, ts[name], Val(name), inds, ns[inds])), names))
 end
 
-#state and next_state have specialized fetch methods due to stack_size
+#state and next_state have specialized fetch methods due to stacksize
 fetch(::NStepBatchSampler{names, Nothing}, trace::AbstractTrace, ::Val{:state}, inds, ns) where {names} = trace[inds]
-fetch(s::NStepBatchSampler{names, Int}, trace::AbstractTrace, ::Val{:state}, inds, ns) where {names} = trace[[x + i for i in -s.stack_size+1:0, x in inds]]
+fetch(s::NStepBatchSampler{names, Int}, trace::AbstractTrace, ::Val{:state}, inds, ns) where {names} = trace[[x + i for i in -s.stacksize+1:0, x in inds]]
 fetch(::NStepBatchSampler{names, Nothing}, trace::RelativeTrace{1,0}, ::Val{:next_state}, inds, ns) where {names} = trace[inds .+ ns .- 1]
-fetch(s::NStepBatchSampler{names, Int}, trace::RelativeTrace{1,0}, ::Val{:next_state}, inds, ns) where {names}  = trace[[x + ns[idx] - 1 + i for i in -s.stack_size+1:0, (idx,x) in enumerate(inds)]]
+fetch(s::NStepBatchSampler{names, Int}, trace::RelativeTrace{1,0}, ::Val{:next_state}, inds, ns) where {names}  = trace[[x + ns[idx] - 1 + i for i in -s.stacksize+1:0, (idx,x) in enumerate(inds)]]
 
 #reward due to discounting
 function fetch(s::NStepBatchSampler, trace::AbstractTrace, ::Val{:reward}, inds, ns)
@@ -247,7 +247,7 @@ function StatsBase.sample(s::NStepBatchSampler{names}, e::EpisodesBuffer{<:Any,
     w = StatsBase.FrequencyWeights(p)
     valids, ns = valid_range(s,e)
     w .*= valids[1:end-1]
-    inds = StatsBase.sample(s.rng, eachindex(w), w, s.batch_size)
+    inds = StatsBase.sample(s.rng, eachindex(w), w, s.batchsize)
     merge(
         (key=t.keys[inds], priority=p[inds]),
         fetch(s, e, Val(names), inds, ns)
@@ -297,3 +297,73 @@ function StatsBase.sample(::EpisodesSampler, t::EpisodesBuffer, names)
 
     return [make_episode(t, r, names) for r in ranges]
 end
+
+#####MultiStepSampler
+
+"""
+    MultiStepSampler{names}(batchsize, stacksize, n, rng)
+
+A sampler that returns n consecutive steps of each trace. The samples are
+returned in an array of batchsize elements. For each element, n is truncated by the end
+of its episode. This means that the dimensions of each sample are not the same.  
+"""
+struct MultiStepSampler{names, S <: Union{Nothing,Int}, R <: AbstractRNG}
+    n::Int
+    batchsize::Int
+    stacksize::Int
+    rng::R 
+end
+
+MultiStepSampler(t::AbstractTraces; kw...) = MultiStepSampler{keys(t)}(; kw...)
+function MultiStepSampler{names}(; n, batchsize=32, stacksize=nothing, rng=Random.default_rng()) where {names} 
+    @assert n >= 1 "n must be ≥ 1."
+    ss = stacksize == 1 ? nothing : stacksize
+    MultiStepSampler{names, typeof(ss), typeof(rng)}(n, batchsize, ss, rng)
+end
+
+function valid_range(s::MultiStepSampler, eb::EpisodesBuffer) 
+    range = copy(eb.sampleable_inds)
+    ns = Vector{Int}(undef, length(eb.sampleable_inds))
+    stacksize = isnothing(s.stacksize) ? 1 : s.stacksize 
+    for idx in eachindex(range)
+        step_number = eb.step_numbers[idx]
+        range[idx] = step_number >= stacksize && eb.sampleable_inds[idx]
+        ns[idx] = min(s.n, eb.episodes_lengths[idx] - step_number + 1)
+    end
+    return range, ns
+end
+
+function StatsBase.sample(s::MultiStepSampler{names}, ts) where {names}
+    StatsBase.sample(s, ts, Val(names))
+end
+
+function StatsBase.sample(s::MultiStepSampler, t::EpisodesBuffer, ::Val{names}) where names
+    weights, ns = valid_range(s, t)
+    inds = StatsBase.sample(s.rng, 1:length(t), StatsBase.FrequencyWeights(weights[1:end-1]), s.batchsize)
+    fetch(s, t, Val(names), inds, ns)
+end
+
+function fetch(s::MultiStepSampler, ts::EpisodesBuffer, ::Val{names}, inds, ns) where names
+    NamedTuple{names}(map(name -> collect(fetch(s, ts[name], Val(name), inds, ns[inds])), names))
+end
+
+function fetch(::MultiStepSampler, trace, ::Val, inds, ns)
+    [trace[idx:(idx + ns[i] - 1)] for (i,idx) in enumerate(inds)]
+end
+
+function fetch(s::MultiStepSampler{names, Int}, trace::AbstractTrace, ::Union{Val{:state}, Val{:next_state}}, inds, ns) where {names} 
+    [trace[[idx + i + n - 1 for i in -s.stacksize+1:0, n in 1:ns[j]]] for (j,idx) in enumerate(inds)]
+end
+
+function StatsBase.sample(s::MultiStepSampler{names}, e::EpisodesBuffer{<:Any, <:Any, <:CircularPrioritizedTraces}) where {names}
+    t = e.traces
+    p = collect(deepcopy(t.priorities))
+    w = StatsBase.FrequencyWeights(p)
+    valids, ns = valid_range(s,e)
+    w .*= valids[1:end-1]
+    inds = StatsBase.sample(s.rng, eachindex(w), w, s.batchsize)
+    merge(
+        (key=t.keys[inds], priority=p[inds]),
+        fetch(s, e, Val(names), inds, ns)
+    )
+end

test/samplers.jl

@@ -78,9 +78,9 @@ import ReinforcementLearningTrajectories.fetch
         γ = 0.99
         n_stack = 2
         n_horizon = 3
-        batch_size = 1000
+        batchsize = 1000
         eb = EpisodesBuffer(CircularArraySARTSATraces(capacity=10)) 
-        s1 = NStepBatchSampler(eb, n=n_horizon, γ=γ, stack_size=n_stack, batch_size=batch_size)
+        s1 = NStepBatchSampler(eb, n=n_horizon, γ=γ, stacksize=n_stack, batchsize=batchsize)
 
         push!(eb, (state = 1, action = 1))
         for i = 1:5
@@ -98,12 +98,12 @@ import ReinforcementLearningTrajectories.fetch
         for key in keys(eb)
             @test haskey(batch, key)
         end
-        #state: samples with stack_size
+        #state: samples with stacksize
         states = ReinforcementLearningTrajectories.fetch(s1, eb[:state], Val(:state), inds, ns[inds])
         @test states == [1 2 3 4 7 8 9;
                          2 3 4 5 8 9 10]
         @test all(in(eachcol(states)), unique(eachcol(batch[:state])))
-        #next_state: samples with stack_size and nsteps forward
+        #next_state: samples with stacksize and nsteps forward
         next_states = ReinforcementLearningTrajectories.fetch(s1, eb[:next_state], Val(:next_state), inds, ns[inds])
         @test next_states == [4 5 5 5 10 10 10;
                               5 6 6 6 11 11 11]
@@ -127,9 +127,9 @@ import ReinforcementLearningTrajectories.fetch
         ### CircularPrioritizedTraces and NStepBatchSampler
         γ = 0.99
         n_horizon = 3
-        batch_size = 4
+        batchsize = 4
         eb = EpisodesBuffer(CircularPrioritizedTraces(CircularArraySARTSATraces(capacity=10), default_priority = 10f0)) 
-        s1 = NStepBatchSampler(eb, n=n_horizon, γ=γ, batch_size=batch_size)
+        s1 = NStepBatchSampler(eb, n=n_horizon, γ=γ, batchsize=batchsize)
 
         push!(eb, (state = 1, action = 1))
         for i = 1:5
@@ -196,4 +196,48 @@ import ReinforcementLearningTrajectories.fetch
         @test length(b[2][:state]) == 5
         @test !haskey(b[1], :action)
     end
+    @testset "MultiStepSampler" begin
+        n_stack = 2
+        n_horizon = 3
+        batchsize = 1000
+        eb = EpisodesBuffer(CircularArraySARTSATraces(capacity=10)) 
+        s1 = MultiStepSampler(eb, n=n_horizon, stacksize=n_stack, batchsize=batchsize)
+
+        push!(eb, (state = 1, action = 1))
+        for i = 1:5
+            push!(eb, (state = i+1, action =i+1, reward = i, terminal = i == 5))
+        end
+        push!(eb, (state = 7, action = 7))
+        for (j,i) = enumerate(8:11)
+            push!(eb, (state = i, action =i, reward = i-1, terminal = false))
+        end
+        weights, ns = ReinforcementLearningTrajectories.valid_range(s1, eb)
+        @test weights == [0,1,1,1,1,0,0,1,1,1,0]
+        @test ns == [3,3,3,2,1,-1,3,3,2,1,0] #the -1 is due to ep_lengths[6] being that of 2nd episode but step_numbers[6] being that of 1st episode
+        inds = [i for i in eachindex(weights) if weights[i] == 1]
+        batch = sample(s1, eb)
+        for key in keys(eb)
+            @test haskey(batch, key)
+        end
+        #state and next_state: samples with stacksize
+        states =  ReinforcementLearningTrajectories.fetch(s1, eb[:state], Val(:state), inds, ns[inds])
+        @test states == [[1 2 3; 2 3 4], [2 3 4; 3 4 5], [3 4; 4 5], [4; 5;;], [7 8 9; 8 9 10], [8 9; 9 10], [9; 10;;]]
+        @test all(in(states), batch[:state])
+        #next_state: samples with stacksize and nsteps forward
+        next_states = ReinforcementLearningTrajectories.fetch(s1, eb[:next_state], Val(:next_state), inds, ns[inds])
+        @test next_states == [[2 3 4; 3 4 5], [3 4 5; 4 5 6], [4 5; 5 6], [5; 6;;], [8 9 10; 9 10 11], [9 10; 10 11], [10; 11;;]]
+        @test all(in(next_states), batch[:next_state])
+        #all other traces sample normally
+        actions = ReinforcementLearningTrajectories.fetch(s1, eb[:action], Val(:action), inds, ns[inds])
+        @test actions == [[2,3,4], [3,4,5], [4,5], [5], [8,9,10], [9,10],[10]]
+        @test all(in(actions), batch[:action])
+        next_actions = ReinforcementLearningTrajectories.fetch(s1, eb[:next_action], Val(:next_action), inds, ns[inds])
+        @test next_actions == [a .+ 1 for a in [[2,3,4], [3,4,5], [4,5], [5], [8,9,10], [9,10],[10]]]
+        @test all(in(next_actions), batch[:next_action])
+        rewards = ReinforcementLearningTrajectories.fetch(s1, eb[:reward], Val(:reward), inds, ns[inds])
+        @test rewards == actions
+        @test all(in(rewards), batch[:reward])
+        terminals = ReinforcementLearningTrajectories.fetch(s1, eb[:terminal], Val(:terminal), inds, ns[inds])
+        @test terminals == [[a == 5 ? 1 : 0 for a in acs] for acs in actions]
+    end
 end