File size: 3,847 Bytes
68d47a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | """PALIMPSESTE — End-to-end integration demo.
Runs the full autonomous loop on a small deterministic environment (a ring of
states) and prints telemetry showing:
- surprise decreasing as the agent learns the transition
- memory growing append-only (no retraining)
- consolidation producing abstract concepts
- the meta-controller occasionally rewriting its kernel under the Lyapunov
constraint (audit-logged in H_meta)
Run: python examples/quickstart.py
"""
from __future__ import annotations
import numpy as np
from palimseste import hv
from palimseste.loop import Palimseste, Environment, LoopConfig
class RingEnv(Environment):
"""A ring of N states; each tick advances to the next state.
Adjacent states share most bits, so the transition o_t -> o_{t+1} is
learnable by associative memory.
"""
def __init__(self, D: int, n_states: int = 5, seed: int = 0):
self.D = D
rng = np.random.default_rng(seed)
base = hv.random_hv(D=D, rng=rng)
self.states: list[hv.HV] = [base]
signs = hv.bits_to_signs(base)
bps = max(1, D // 80)
cur = signs.copy()
for _ in range(n_states - 1):
flip = rng.choice(D, size=bps, replace=False)
cur = cur.copy()
cur[flip] = -cur[flip]
self.states.append(hv.signs_to_bits(cur))
self._i = 0
self._advance = hv.random_hv(D=D, rng=rng)
def observe(self) -> hv.HV:
return self.states[self._i]
def actions(self) -> list[hv.HV]:
return [self._advance, hv.random_hv(D=self.D)] # 2 actions: advance / noop
def act(self, action: hv.HV) -> None:
# action 0 (advance) moves the ring; action 1 (noop) stays
if action is self._advance:
self._i = (self._i + 1) % len(self.states)
def done(self) -> bool:
return False
def main() -> None:
D = 3000
agent = Palimseste(
D=D,
rng=np.random.default_rng(0),
loop_cfg=LoopConfig(
surprise_threshold=0.2,
consolidate_every=24,
meta_every=96,
max_radius=150,
),
)
env = RingEnv(D=D, n_states=5, seed=0)
print("=" * 72)
print("PALIMPSESTE — autonomous active-inference loop demo")
print(f"D={D} ring_states=5 ticks=600")
print("=" * 72)
surprises: list[float] = []
for t in range(1, 601):
r = agent.step(env)
surprises.append(r.surprise)
if t % 100 == 0 or t == 1:
recent = np.mean(surprises[max(0, t - 50):t])
stats = agent.stats()
print(
f"t={t:4d} surprise={r.surprise:.3f} "
f"mean(50)={recent:.3f} |M|={stats['n_traces']:5d} "
f"concepts={stats['n_concepts']:3d} "
f"meta_dec={stats['n_meta_decisions']:3d} "
f"kernel={stats['kernel']}"
)
if r.consolidation is not None and r.consolidation.promoted:
print(f" consolidated {len(r.consolidation.promoted)} concept(s)")
if r.meta is not None and r.meta.accepted:
print(
f" META rewrite ACCEPTED: "
f"ΔL={r.meta.delta:+.4f} -> {r.meta.proposal.config.encode()}"
)
print("=" * 72)
first = np.mean(surprises[:50])
last = np.mean(surprises[-50:])
print(f"surprise: first-50 mean = {first:.3f} last-50 mean = {last:.3f}")
print(f"Δ = {last - first:+.3f} ({'decreased ✓' if last < first else 'NOT decreased ✗'})")
stats = agent.stats()
print(f"final |M| = {stats['n_traces']} traces, "
f"{stats['n_concepts']} concepts, "
f"{stats['n_meta']} meta-traces (H_meta audit log)")
print("=" * 72)
if __name__ == "__main__":
main()
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