80 lines
3.0 KiB
Nim
80 lines
3.0 KiB
Nim
## Assert-based tests for rewards.nim.
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## Run: nim c -r tests/test_rewards.nim
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import std/[math, strformat]
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import SAC_LSTM_Bot/rewards
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template check(cond: bool, msg: string) =
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if not cond:
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quit("FAIL: " & msg, 1)
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# ── computeReward ─────────────────────────────────────────────────────────────
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block damageInflicted:
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# p=1: 6*1 - 2 = 4
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check abs(computeReward(damageInflicted = 1.0) - 4.0) < 1e-9, "p=1 damage = +4"
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# p=3: 6*3 - 2 = 16
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check abs(computeReward(damageInflicted = 3.0) - 16.0) < 1e-9, "p=3 damage = +16"
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block damageReceived:
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# p_e=1: -(6*1 - 2) = -4
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check abs(computeReward(damageReceived = 1.0) - (-4.0)) < 1e-9, "p_e=1 received = -4"
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# p_e=3: -(6*3 - 2) = -16
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check abs(computeReward(damageReceived = 3.0) - (-16.0)) < 1e-9, "p_e=3 received = -16"
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block wallHit:
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check abs(computeReward(wallHitTicks = 1) - (-5.0)) < 1e-9, "1 wall tick = -5"
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block wastedShot:
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# p=2: -0.1 * 2 = -0.2
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check abs(computeReward(wastedShotPower = 2.0) - (-0.2)) < 1e-9, "missed p=2 = -0.2"
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block winLoss:
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check abs(computeReward(win = true) - 20.0) < 1e-9, "win = +20"
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check abs(computeReward(loss = true) - (-10.0)) < 1e-9, "loss = -10"
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# ── RewardNormalizer cold start ───────────────────────────────────────────────
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block coldStart:
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var rn: RewardNormalizer
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# 0 samples
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let v0 = rn.normalize(99.0)
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check not isNaN(v0), "0 samples: not NaN"
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check classify(v0) != fcInf and classify(v0) != fcNegInf, "0 samples: not inf"
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check abs(v0) < 1e-9, "0 samples: returns 0"
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# 1 sample (variance undefined)
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rn.update(5.0)
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let v1 = rn.normalize(5.0)
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check not isNaN(v1), "1 sample: not NaN"
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check classify(v1) != fcInf and classify(v1) != fcNegInf, "1 sample: not inf"
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check abs(v1) < 1e-9, "1 sample: returns 0"
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# ── Running normalization convergence ─────────────────────────────────────────
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block convergence:
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var rn: RewardNormalizer
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# Feed 1000 identical samples of 5.0 — mean=5.0, std=0 → normalizer returns ~0
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for _ in 0 ..< 1000:
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rn.update(5.0)
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let v = rn.normalize(5.0)
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check not isNaN(v), "convergence: not NaN"
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check classify(v) != fcInf and classify(v) != fcNegInf, "convergence: not inf"
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# (5 - 5) / (0 + eps) = 0
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check abs(v) < 1e-6, "convergence to mean: normalized ≈ 0"
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block knownMeanStd:
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# Insert samples -1 and +1 repeatedly → mean=0, std=1
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var rn: RewardNormalizer
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for _ in 0 ..< 500:
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rn.update(-1.0)
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rn.update( 1.0)
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# normalize(1.0) ≈ (1 - 0) / (1 + eps) ≈ 1
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let vPos = rn.normalize(1.0)
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check abs(vPos - 1.0) < 1e-4, &"normalize(+1) ≈ +1, got {vPos}"
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let vNeg = rn.normalize(-1.0)
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check abs(vNeg - (-1.0)) < 1e-4, &"normalize(-1) ≈ -1, got {vNeg}"
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let vMid = rn.normalize(0.0)
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check abs(vMid) < 1e-4, &"normalize(0) ≈ 0, got {vMid}"
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echo "test_rewards: all passed"
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