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Bio Player — Environment 3

1. Overview

env3_player_bio.py is a pointwise-activation Echo State Network controller for Environment 3:

X(t+1) = f(Win @ I(t) + W @ X(t))
O(t+1) = Wout @ g(X(t+1))        (g = identity)

Every hidden activation is a scalar function of its own preactivation; all cross-neuron logic lives in the connectivity matrices. The model is produced by a single yield in bio_player(), so the matrices are fixed at build time (no iterative training).

Env3 exposes colour (the sources are coloured), but this controller does not read colour or energy. The bot runs around the outer corridor with a reflex wall-follower and picks up whatever source it happens to cross. Only 7 of the 1000 hidden slots receive any incoming weight; the rest are dead.

2. Network shape

Parameter Value
n 1000
p 64 (camera rays)
n_inputs 2*p + 3 = 131
warmup 0
leak (λ) 1.0
g identity
Actuator clip step_a = 5°

Module constants: front_gain_mag = 20°, step_a = 5°.

3. Activations

Name Formula Used by
relu_tanh max(0, tanh(z)) reflex channels (slots 0..4), front-block
clip_a clip(z, -step_a, +step_a) dtheta

4. Inputs and slot layout

I(t) = [prox[0..63](t), colour[0..63](t), hit(t), energy(t), 1]

Taps used by the controller (colour and energy columns are unread):

L_idx          = 20      (left reflex proximity tap)
R_idx          = 43      (right reflex proximity tap)
left_side_idx  = 11      (left safety tap)
right_side_idx = 52      (right safety tap)
C1_idx, C2_idx = 31, 32  (centre-front proximity taps)
hit_idx        = 128     (= 2*p)
bias_idx       = 130     (= 2*p + 2)

Hidden slots (n = 1000; slots 7..999 are dead):

Slot Name Activation Role
0 hit_feat relu_tanh hit reflex
1 prox_left relu_tanh left proximity reflex
2 prox_right relu_tanh right proximity reflex
3 safe_left relu_tanh left safety feature
4 safe_right relu_tanh right safety feature
5 dtheta clip_a one-step-lagged steering command
6 front_block relu_tanh unsigned front-block detector

5. Circuits

5.1 Reflex features and readout

Five feed-forward proximity/hit detectors:

hit_feat(t+1)   = relu_tanh(hit(t))
prox_left(t+1)  = relu_tanh(prox[L_idx](t))
prox_right(t+1) = relu_tanh(prox[R_idx](t))
safe_left(t+1)  = relu_tanh(-prox[left_side_idx](t)  + 0.75)
safe_right(t+1) = relu_tanh(-prox[right_side_idx](t) + 0.75)

Steering readout:

O(t+1) = hit_turn          * hit_feat(t+1)
       + heading_gain      * prox_left(t+1)
       - heading_gain      * prox_right(t+1)
       + safety_gain_left  * safe_left(t+1)
       + safety_gain_right * safe_right(t+1)
       + front_gain_mag    * front_block(t+1)

with

hit_turn          = -10° / tanh(1)
heading_gain      = -40°
safety_gain_left  = -20°
safety_gain_right = +20°
front_gain_mag    = +20°

dtheta holds the clipped one-step-lagged command, dtheta(t+1) = clip(O(t), ±step_a), implemented by mirroring the Wout row into W[dtheta, :].

5.2 Front block

Unsigned sum of the two centre proximity taps:

front_block(t+1) = relu_tanh(prox[C1_idx](t) + prox[C2_idx](t) - 1.4)

A positive reading turns the bot by +20° (CCW) — a fixed-direction escape that keeps the bot on the outer corridor.

6. Nonzero readout weights

Wout[hit_feat]    = -10° / tanh(1)
Wout[prox_left]   = -40°
Wout[prox_right]  = +40°
Wout[safe_left]   = -20°
Wout[safe_right]  = +20°
Wout[front_block] = +20°

Six nonzero entries total. The same row is mirrored into W[dtheta, :].

7. Verification

python braincraft/env3_player_bio.py

Runs train(bio_player, timeout=100) then evaluate(model, Bot, Environment, debug=False, seed=12345) over 10 episodes:

Final score (distance): 14.40 +/- 0.49

500-seed sweep (validate_env3_player_bio.py, seeds 0..499): across-seed mean 14.50 ± 0.17, min 14.00, 0/500 seeds below 13.50.