Analyzed network training game from weight patternsPublic

Agent

Codex

Model

GPT 6 Astra

Started

Nov 3, 3:08 AM

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Summary

In Progress

Understand
Reviewed grid and channel observations.
Understand
Understand
Analyzed weight patterns across positions and channels.
Understand

THE OBSERVATION The network sees a 10 x 10 grid. Rows are numbered 0 (top) to 9 (bottom) and columns 0 (left) to 9 (right). The outer ring of cells (row 0, row 9, column 0 and column 9) is a wall border; the playable area is the inner 8 x 8 cells (rows 1-8, columns 1-8). Each cell has 5 binary channels (0 or 1): channel 0 agent: the cells the player controls channel 1 moving objects: things other than the agent that move channel 2 reward targets: things that give reward channel 3 obstacles: cells the agent cannot enter or that end the episode (the wall border is always here) channel 4 previous frame: the cells that were in channel 0 or channel 1 on the previous step The observation is flattened to 500 numbers. Input number i = (row * 10 + column) * 5 + channel; for example input 0 is (row 0, column 0, channel 0), input 4 is (row 0, column 0, channel 4), input 5 is (row 0, column 1, channel 0) and input 499 is (row 9, column 9, channel 4).

THE ACTIONS The network outputs 5 numbers (logits), one per action; the action is sampled from the softmax of these logits: action 0 no-op action 1 up (toward row 0) action 2 down (toward row 9) action 3 left (toward column 0) action 4 right (toward column 9)

THE NETWORK A policy network (multilayer perceptron) with 500 inputs, two hidden layers of 32 units with tanh activations, and 5 outputs: h1 = tanh(x @ W1 + b1) W1: 500 x 32, b1: 32 h2 = tanh(h1 @ W2 + b2) W2: 32 x 32, b2: 32 logits = h2 @ W3 + b3 W3: 32 x 5, b3: 5 Row i of W1 holds the weights from input i to the 32 hidden units; column j of a weight matrix belongs to unit j of the next layer. 17,253 parameters in total. It was trained with reinforcement learning (PPO) to maximise the reward of a game played on this grid.

QUESTION. The weights below belong to one policy network that was trained with reinforcement learning to play a game on this grid. Which game was it trained on? Work from the weights. You have no tools: no code execution, no calculator, nothing to run. This is a single response.

Answer in this order: Observations: <what you see in the weights: for each input channel, where in the grid its weights are large or show structure and what that suggests; what the output layer and its biases show about the actions; quote the weights you rely on> Reasoning: <which kinds of game fit these observations, which are ruled out, and why> Game: <the game, as specifically as you can; if it resembles a well-known game, give that game's name> Description: <what the agent does and what gives reward, in one or two sentences> Confidence: <0-100>%

THE NETWORK'S WEIGHTS Policy network weights. Forward pass: h1 = tanh(x @ W1 + b1); h2 = tanh(h1 @ W2 + b2); logits = h2 @ W3 + b3. Values rounded to 3 decimals.

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b1 (layer 1 biases), shape 32: 0.048 0.185 -0.041 -0.492 -0.036 -0.067 -0.161 0.190 -0.188 0.215 -0.021 0.141 -0.245 0.366 0.225 -0.233 0.185 -0.068 0.007 0.193 0.075 -0.003 0.322 0.183 0.035 0.434 -0.002 0.029 0.434 -0.220 0.195 0.083

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1.268 -1.098 -0.070 0.494 -0.105 -0.241 -0.082 -0.584 1.099 -1.279 0.819 0.963 -0.013 -0.056 -1.103 0.090 0.630 0.459 1.059 0.886 -0.105 0.112 -1.532 -0.308 -0.618 0.554 0.226 -1.362 -0.785 0.297 1.068 -0.167 -0.684 -0.429 -0.582 -0.329 0.136 -0.797 0.259 0.372 0.102 -0.089 0.301 -0.285 -0.022 0.805 -1.297 -0.433 0.488 -0.465 -1.378 -0.410 -0.770 -0.680 -0.617 0.743 -0.857 -0.366 -1.154 0.644 0.053 0.398 -0.967 -1.149 -0.024 -0.031 1.726 0.826 -0.514 -0.250 0.457 -1.100 0.608 -0.866 0.251 1.168 -1.052 0.375 -0.632 0.349 -0.153 -0.085 0.385 -0.097 0.838 0.067 0.415 0.128 -0.248 -0.978 -0.613 0.121 -0.225 1.044 -0.826 -0.699 0.292 0.073 0.403 0.433 -0.783 -0.918 -0.059 0.342 0.003 -0.430 0.075 0.809 0.472 0.011 -0.271

b2 (layer 2 biases), shape 32: 0.612 0.193 -1.216 -0.395 0.209 0.814 1.132 1.027 -1.043 1.396 0.093 -0.949 0.414 0.787 0.373 0.355 -0.505 0.503 -0.437 0.693 0.761 -0.888 -0.139 -0.683 -0.395 -0.326 -0.830 0.351 0.595 -0.358 0.844 0.328

W3 (output layer weights), shape 32 x 5: one line per row -1.088 1.090 -0.527 1.054 0.542 1.693 0.234 1.430 -0.553 -1.620 0.778 -1.273 0.935 0.988 -0.254 0.300 -0.632 0.837 1.649 1.492 -0.608 0.179 -0.426 -0.411 -1.139 -1.312 0.302 -1.949 0.983 -2.010 0.023 2.626 -0.658 0.268 -2.432 -0.195 1.114 0.885 0.276 -0.731 -0.044 -2.585 0.909 -0.191 1.541 -0.908 1.042 -1.531 -0.470 -1.760 -0.714 0.660 -0.182 1.364 0.548 1.589 -1.980 1.042 1.719 0.880 0.626 -0.489 0.003 -0.649 -0.987 -0.552 1.898 -1.643 -2.170 -0.299 -0.848 0.923 -0.795 -1.316 -0.957 0.713 1.403 -0.320 -0.044 -0.794 1.371 -0.577 0.711 0.829 0.436 -1.340 2.194 0.141 -0.351 1.609 -0.384 -0.184 -0.132 -1.981 0.727 -1.473 0.569 -1.380 0.252 -0.461 -0.096 0.277 -0.425 1.314 -1.687 0.096 -0.656 -0.169 1.096 2.150 -0.959 -0.532 -0.493 1.044 -0.823 0.835 -1.385 0.131 0.973 0.652 -0.773 -0.636 -0.423 1.861 0.761 0.612 2.235 -0.387 -1.528 0.141 1.501 -0.182 0.320 -0.811 1.483 -0.206 0.526 1.573 -1.023 1.685 -1.315 2.473 0.311 0.312 0.138 -0.214 0.134 -1.070 -1.691 2.315 -1.090 1.062 -1.877 -0.486 0.239 1.000 0.734 0.111 -0.991 -0.128

b3 (output layer biases), shape 5: -0.029 3.142 -1.862 0.173 -1.269

Observations:

Channel 0 — agent: Strong, position-dependent weights cover rows 2–8, rather than a single paddle row. For example, at (2,4) the weights include 4.331, −2.970, 3.294, −3.827. This suggests an agent navigating across the board.
Channel 1 — moving objects: Strong structure occupies rows 2–7, with striking horizontal repetition. At (2,1), hidden units 18 and 25 receive −1.898, 1.562; at (2,5) they receive −1.879, 1.611. At (3,2) and (3,6), unit 12 receives 6.639 and 6.689. These four-column repetitions suggest regularly spaced traffic in horizontal lanes. Row 8 looks like a safe starting strip.
Channel 2 — reward targets: The conspicuous structure is across row 1, columns 1–8, not scattered throughout the interior. At (1,1), weights include −0.550 to unit 3, 0.440 to unit 25, and 0.464 to unit 28. The similar vectors across this row suggest a fixed destination spanning the top.
Channel 3 — obstacles: The border has nearly identical learned vectors; interior weights are generally small and unstructured. For example, (0,0) has −0.477, 0.440, 0.437 at units 3, 25, and 28. These resemble the corresponding biases −0.492, 0.434, 0.434, consistent with always-present border inputs acting as additional biases—not evidence of an interior maze.
Channel 4 — previous frame: Substantial structure appears in the traffic region and at agent positions. At (2,1), weights include −1.443 and −2.266; at (2,5), −0.786 and −2.405. Temporal information therefore matters, fitting movement prediction and crossing timing.
Outputs: The biases strongly favor up: [−0.029, 3.142, −1.862, 0.173, −1.269]. However, all directions retain substantial state-dependent connections: output-layer unit 6 has 2.626 toward up and −2.432 toward right; unit 29 has −1.691 toward left and 2.315 toward right. This fits upward progress with situational evasive moves; biases alone do not establish action frequencies.

Reasoning: The combination of a bottom safe strip, several horizontally periodic moving-object lanes, a top destination, and a strong upward action bias points directly to a road-crossing game. Previous-frame processing supports timing crossings around moving traffic.

Pong or Breakout would normally restrict the agent to a paddle line. Snake and food-collection games would require substantial reward-target structure throughout the interior. A conventional maze would show interior obstacle structure. Those alternatives fit much less well.

Game: Frogger — a simplified grid-based traffic-crossing variant.

Description: The agent moves from the bottom toward the top, timing its movements to avoid horizontally moving traffic. Reaching the top destination gives reward.

Confidence: 97%