Analyzed network training for Breakout game
Public
Analyzed network training for Breakout game
Agent
Codex
Model
GPT 6 Astra
Started
Nov 3, 3:08 AM
Messages3
Downloads0
Reviewed grid observation and channel definitions.
UnderstandAnalyzed weight patterns for agent and objects.
UnderstandInferred game type from network weights.
VerifyTHE 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: 0.024 0.199 -0.022 -0.144 0.104 -0.144 -0.142 0.145 -0.107 -0.022 -0.023 0.016 0.186 0.077 0.034 0.062 -0.040 -0.088 -0.079 0.133 0.097 -0.007 0.083 -0.023 -0.014 0.027 -0.143 -0.166 -0.037 -0.129 -0.064 0.012
W2 (layer 2 weights), shape 32 x 32: one line per row 1.834 -0.104 1.259 0.248 0.186 0.139 -0.697 0.151 -0.344 -0.145 -1.270 0.705 1.170 0.039 0.596 -0.766 -0.943 -1.180 0.272 0.159 0.395 -0.070 -0.146 -0.686 0.211 -0.184 0.125 -1.100 0.087 -0.277 -0.294 -0.103 -0.074 0.029 -0.427 0.190 0.072 0.178 0.111 -1.077 -0.293 -0.105 0.054 0.784 0.205 0.419 0.854 -0.246 1.055 -0.052 -0.086 -1.104 -0.083 0.467 0.997 -0.478 0.177 -0.994 0.662 -0.455 -0.004 -0.154 -0.144 0.287 -0.204 0.798 -0.019 0.750 0.064 -2.312 -0.469 0.558 0.433 2.225 -1.259 1.325 -0.484 1.308 0.182 0.839 0.006 -1.100 1.193 -0.249 -0.061 0.895 -1.415 0.131 -1.102 -0.715 0.106 0.029 1.811 0.179 -2.031 0.146 -0.091 -0.063 1.351 -0.118 0.434 -0.255 0.632 -0.135 0.741 0.240 -0.264 0.226 -0.164 0.283 0.062 -0.175 0.156 -0.215 0.148 1.418 0.209 0.765 -0.288 -0.492 -0.589 -0.371 0.726 -0.660 0.139 -1.038 -0.525 0.410 -0.144 0.500 0.916 0.186 -0.155 -0.682 -0.358 0.755 0.241 0.464 -0.006 -0.313 -0.662 0.280 0.084 0.229 0.734 -0.080 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0.018 1.339 0.650 0.331 -0.324 -0.754 -0.974 -0.545 0.947 0.426 -0.906 1.191 1.322 -0.960 -0.784 1.133 -0.219 -0.192 -0.154 1.212 -0.185 0.867 0.586 0.269 0.202 -0.499 -0.108 0.563 -0.102 0.123 1.131 0.253 -0.249 0.528 0.671 0.727 0.373 -0.519 0.188 -1.687 0.282 -0.699 0.284 0.409 1.741 0.001 -1.785 1.594 1.352 0.164 -0.001 -0.703 0.233 1.169 0.059 1.647 -0.160 -1.172 0.296 0.186 1.657 1.028 0.924 0.862 -0.293 -0.780 -0.338 -1.342 -0.086 0.311 0.028 0.308 -0.146 0.985 -0.963 0.429 0.500 -0.556 0.462 -0.400 -0.035 1.260 0.238 -0.439 -0.847 0.355 1.918 0.222 -1.080 -0.183 -0.088 -0.340 0.634 -2.245 0.076 -1.065 -0.448 -0.255 -0.646 1.464 1.854 -1.087 -0.166 0.822 0.612 -2.074 0.742 -0.615 -0.737 -1.722 1.021 -1.255 -0.978 0.260 -0.047 -1.030 1.524 0.396 -0.414 -0.505 0.920 1.139 0.654 -0.747 -1.305 -0.392 -1.434 -1.017 -0.670 -0.501 2.027 0.308 0.018 -0.128 -1.482 2.459 -0.283 -0.269 -1.611 -0.260 -0.369 0.520 2.611 -2.243 -0.754 0.561 -0.558 0.996 0.175 0.662 1.374 -1.635 0.057 -1.977 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0.270 2.222 1.393 -0.085 -1.305 0.655 -0.264 1.076 0.014 -1.823 1.078 0.154 1.176 -0.170 -0.396 -0.176 0.191 -0.514 0.117 0.079 0.118 0.284 0.235 -0.836 0.192 0.486 -0.557 0.256 -0.282 0.830 -0.367 -0.656 -0.381 -0.562 0.361 0.913 0.262 -1.743 -0.005 0.206 -0.344 0.584 0.551 -0.620 -0.033 -1.499 -0.410 1.771 0.317 -0.159 -0.056 -2.324 1.383 -1.284 0.565 -0.584 -0.850 -0.690 0.346 1.410 -1.055 0.204 0.085 -1.209 2.017 0.634 0.728 0.145 -0.661 0.684 -2.035 0.192 1.550 -1.204 -0.173 -0.926 -0.307 0.213 0.119 0.948 -0.472 -1.267 0.122 -0.909 -0.254 -0.077 0.256 0.331 -0.225 -0.493 -0.900 0.256 0.337 0.666 0.742 0.200 0.183 -0.251 0.196 0.140 0.278 -0.132 -0.274 0.059 0.255 0.202 -0.068 -0.752 0.388 0.016 -0.235 -0.164 0.056 -0.658 0.174 -0.237 0.334 0.771 0.204 0.170 -0.006 -0.681 -0.797 0.242 0.056 0.220 0.445 -0.687 -0.542 -0.379 0.377 -0.055 0.234 -0.164 -0.323 -0.392 0.230 0.331 0.360 0.423 0.233 0.447 -0.333 -1.231 -0.185 1.190 0.643 1.005 -1.012 0.449 -1.260 0.682 0.318 0.661 -0.364 -1.236 0.433 -0.338 0.017 0.436 -0.556 0.365 -0.480 -0.088 0.472 0.057 0.821 -1.140 -1.209 0.251 0.359 -0.379 0.626 -0.245 0.007 0.628 0.051 -0.286 -0.336 -0.470 0.265 0.262 -0.033 0.445 -0.531 -0.317 -0.656 0.051 -0.195 0.060 -0.705 -0.368 -0.263 -0.850 0.567 0.323 -0.499 0.260 -0.611 -0.412 0.317 -0.206 0.238 0.845 -0.676 -0.262 -0.432 1.674 1.221 0.173 -0.830 -1.928 2.269 -0.683 0.390 0.047 -0.384 -0.371 -0.613 2.262 -2.333 -1.433 0.536 -0.219 0.655 1.063 1.370 -0.322 -2.262 1.224 -1.766 -0.240 0.867 -1.000 2.131 -1.703 -0.196 -1.025 -3.398 0.313 -0.550 -1.003 0.121 -0.192 1.179 0.656 1.897 0.098 -1.964 -1.706 -2.436 1.126 0.251 1.044 2.016 -0.791 1.355 0.314 0.638 2.277 0.107 -0.356 -0.373 0.191 0.066 -0.327
b2 (layer 2 biases), shape 32: -0.188 0.590 -0.599 -0.117 -0.049 -0.752 -0.100 0.011 -0.017 0.578 0.355 -0.551 0.160 -0.165 -0.166 0.312 -0.076 0.381 -0.299 -0.778 0.022 0.199 0.778 0.538 -0.287 0.226 -0.220 0.308 0.540 0.605 -0.240 -0.546
W3 (output layer weights), shape 32 x 5: one line per row -1.025 -2.129 -0.508 -1.733 0.078 1.502 1.718 1.370 1.597 -1.796 -0.068 -1.333 -1.384 -0.441 -2.100 -0.317 -0.054 -1.068 -0.403 -3.063 1.127 0.094 1.375 3.081 0.115 -1.616 -1.409 -1.560 -1.293 1.419 1.035 0.850 0.779 0.931 1.518 0.621 1.241 -0.291 2.324 1.378 -1.090 -0.159 -0.928 -1.629 -2.009 1.179 1.049 1.268 1.659 -0.327 1.460 1.354 1.328 -0.903 1.549 -0.177 0.185 0.450 4.057 -0.414 -0.286 0.283 -1.528 -2.727 -1.200 0.884 1.602 1.334 1.398 -0.152 0.946 0.880 0.802 2.492 -1.654 1.084 1.177 1.580 2.754 -1.673 0.462 1.013 0.549 2.570 1.113 1.534 1.461 1.447 -1.106 1.391 -1.270 -1.182 -1.382 0.462 -1.604 -1.047 -1.294 -1.043 -0.477 -1.395 -0.782 -0.989 -1.499 -1.514 -0.673 -0.570 -0.832 0.144 -1.188 -2.949 0.376 0.387 0.537 -5.336 -1.203 1.539 0.781 0.840 0.460 1.692 0.367 0.561 0.305 0.911 3.031 -1.960 -0.579 -0.441 -1.476 -0.984 -0.683 -1.201 -1.744 -0.215 -1.358 0.607 1.394 0.809 0.496 1.190 1.547 1.395 1.382 1.417 -1.013 1.535 1.638 1.406 -1.549 1.526 -1.645 -1.382 -1.458 -1.346 1.380 -1.264 -1.051 -1.013 -0.268 -1.647
b3 (output layer biases), shape 5: 1.221 1.008 1.024 0.897 1.298
Indices below are zero-based.
Channel 0 — agent: Strong weights are concentrated on row 8, across the playable columns; elsewhere they look mostly like small initialization-scale values. At (8,1), weights include 4.066 to hidden unit 15 and −4.013 to unit 31. This suggests a bottom-mounted paddle moving horizontally, rather than a character roaming the grid.
Channel 1 — moving objects: Large, structured weights cover the interior above the paddle. At (1,3), examples include 3.194, 2.708, −4.791, 3.436 to units 5–8. At (2,2), those weights are 3.408, 2.416, −6.356, 4.570. This is consistent with tracking a ball throughout the playfield.
Channel 2 — reward targets: Learned structure is concentrated in rows 1–3, whereas lower rows have mostly small weights. Examples include 1.620 at (1,2) to unit 25, and −4.811 and −3.236 at (3,1) to units 0 and 20. A band of reward-bearing objects above a paddle strongly suggests bricks. Negative weights do not mean negative reward; these are hidden-feature connections.
Channel 3 — obstacles: Interior weights are mostly small, without a conspicuous maze-like pattern. Border weights have a repeated pattern: for example, (0,0) and (0,1) connect to unit 1 with 0.255 and 0.193, respectively, alongside b1[1] = 0.199. Since the border is always present, its shared structure can function as an additional constant bias.
Channel 4 — previous frame: Strong interior weights closely resemble the moving-object channel. At (1,3), units 7 and 8 receive −4.301, 3.057, compared with −4.791, 3.436 for the current moving object. This supports using successive ball positions to estimate motion; bottom-row structure also accommodates the previous paddle position.
Outputs: Left and right are especially differentiated, while no-op/up/down often have similar coefficients. For example:
This fits horizontal paddle control, with vertical commands potentially equivalent to doing nothing. Output biases [1.221, 1.008, 1.024, 0.897, 1.298] are relatively similar; they do not establish a dominant action independently of the hidden activations.
The combination of a bottom-row paddle, a moving ball above it, upper-row reward targets, and horizontal action specialization is characteristic of brick breaking.
Pong could explain a paddle and ball, but not the substantial upper band of reward targets. Catching falling objects is less consistent with separate, spatially structured targets above the moving object’s playfield. Snake, Pac-Man, and maze navigation would generally require agent-position structure throughout the interior and meaningful movement along both axes.
Breakout — a small grid-based brick-breaking variant.
The agent moves a bottom paddle left and right to keep a bouncing ball in play and direct it into bricks above. Destroying bricks gives reward; missing the ball likely costs a life or ends the episode.
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