Update test for NStepBatchSampler

test/samplers.jl

@@ -87,42 +87,42 @@ import ReinforcementLearningTrajectories.fetch
             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)
+        for (j,i) = enumerate(8:12)
             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
+        @test weights == [0,1,1,1,0,0,1,1,1,0,0]
+        @test ns == [3,3,2,1,-1,3,3,3,2,1,0] #the -1 is due to ep_lengths[5] 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: 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 states == [1 2 3 6 7 8;
+                         2 3 4 7 8 9]
         @test all(in(eachcol(states)), unique(eachcol(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 == [4 5 5 5 10 10 10;
-                              5 6 6 6 11 11 11]
+        @test next_states == [4 4 4 9 10 10;
+                              5 5 5 10 11 11]
         @test all(in(eachcol(next_states)), unique(eachcol(batch[:next_state])))
         #action: samples normally 
         actions = ReinforcementLearningTrajectories.fetch(s1, eb[:action], Val(:action), inds, ns[inds])
-        @test actions == inds
+        @test actions == [3, 4, 5, 8, 9, 10]
         @test all(in(actions), unique(batch[:action]))
         #next_action: is a multiplex trace: should automatically sample nsteps forward
         next_actions = ReinforcementLearningTrajectories.fetch(s1, eb[:next_action], Val(:next_action), inds, ns[inds])
-        @test next_actions == [5, 6, 6, 6, 11, 11, 11]
+        @test next_actions == [6, 6, 6, 11, 12, 12]
         @test all(in(next_actions), unique(batch[:next_action]))
         #reward: discounted sum
         rewards = ReinforcementLearningTrajectories.fetch(s1, eb[:reward], Val(:reward), inds, ns[inds])
-        @test rewards ≈ [2+0.99*3+0.99^2*4, 3+0.99*4+0.99^2*5, 4+0.99*5, 5, 8+0.99*9+0.99^2*10,9+0.99*10, 10]
+        @test rewards ≈ [2+0.99*3+0.99^2*4, 3+0.99*4, 4, 7+0.99*8+0.99^2*9, 8+0.99*9+0.99^2*10,9+0.99*10]
         @test all(in(rewards), unique(batch[:reward]))
         #terminal: nsteps forward
         terminals = ReinforcementLearningTrajectories.fetch(s1, eb[:terminal], Val(:terminal), inds, ns[inds])
-        @test terminals == [0,1,1,1,0,0,0]
+        @test terminals == [0,0,0,0,0,0]
 
         ### CircularPrioritizedTraces and NStepBatchSampler
         γ = 0.99