Analyzed network training for Pong game
Public
Analyzed network training for Pong game
Agent
Codex
Model
GPT 6 Astra
Started
Nov 3, 3:08 AM
Messages3
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Reviewed network observation grid and channel meanings.
UnderstandAnalyzed weight patterns in the network.
UnderstandIdentified training on Pong-like game based on weight distribution.
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: -0.076 -0.136 0.184 0.271 0.085 -0.080 0.123 0.193 -0.225 -0.169 0.006 0.092 -0.056 0.040 -0.071 0.071 -0.132 -0.002 -0.112 0.061 0.248 -0.054 0.130 0.082 -0.225 0.164 0.072 0.059 -0.176 -0.083 0.065 0.071
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0.477 -0.243 1.116 1.076 -0.790 -1.746 1.467 3.176 1.107 0.412 -1.250 -1.616 -2.610 -0.695 -0.758 1.539 -0.865 2.371 0.923 2.481 -0.791 0.329 -1.257 1.286 1.415 0.838 -0.737 1.473 -0.825 -2.702 1.494 -2.492 0.897 -2.793 -0.901 1.256 -1.527 -0.486 -0.014 0.557 -0.439 1.566 -1.076 -0.020 -1.194 -1.395 -0.461 0.746 0.855 -0.324 -1.456 0.044 0.600 -0.823 -1.005 0.364 0.057 -1.166 -0.129 -0.384 0.255 -0.669 1.511 -1.395 -2.136 -1.365 -0.310 0.480 -1.220 -0.465 0.199 -0.098 -0.187 0.577 -0.353 0.064 -0.196 -0.970 1.635 -1.376 0.580 0.568 0.036 -0.222 -1.920 -1.132 0.050 -0.360 0.335 -0.119 0.110 0.165 0.106 0.033 -0.415 1.086 0.863 0.009 -0.449 -0.146 0.493 -0.696 -0.086 1.540 0.329 -0.058 -0.721 0.509 0.273 -0.760 -1.813 -0.318 -0.642 0.659 -1.639 0.630 -0.437 -0.931 -0.145 0.295 1.361 -1.381 0.590 0.380 0.326 -0.146 -0.675 -0.549 0.780 -0.061 1.112 0.590 -0.275 -0.165 -0.249 -0.385 0.086 1.001 0.344 0.596 0.981 -0.940 -0.323 0.661 0.607 -0.940 -0.134 0.106 0.649 0.182 0.539 -0.686 1.015 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-0.670 0.147 0.082 -0.714 -0.211 -0.773 -0.002 1.142 0.793 1.390 -0.394 0.043 -0.153 -0.726 -0.256 -0.515 0.321 0.748 -0.804 1.076 1.064 -0.069 1.170 1.785 -0.020 -0.665 -1.664 -0.583 -0.845 -0.249 1.153 1.418 0.499 0.486 -2.419 -1.436 1.989 -1.031 -0.564 -0.469 0.065 -1.626 0.033 1.905 0.385 0.769 -0.280 0.321 -0.116 -1.658 0.949 0.635 -0.130 -0.239 -0.959 -0.673 0.070 0.862 1.347 -1.181 0.109 0.020 1.250 1.154 0.330 -0.167 0.740 1.016 -0.368 1.560 1.442 0.571 0.279 0.479 -0.398 -0.907 0.593 0.640 -0.169 0.739 -0.847 0.249 -0.905 -0.776 2.131 0.095 -1.325 0.979 -2.417 0.171 -0.061 -1.487 -0.013 -0.823 2.081 -1.584 1.154 1.271 -0.042 -0.455 0.611 0.869 0.076 -0.534 1.120 -0.788 -2.635 1.453 -2.006 -0.767 -1.702 -0.543 1.563 0.607
b2 (layer 2 biases), shape 32: 0.205 0.217 -0.150 0.167 0.443 0.357 -0.695 -0.133 0.105 -0.042 -0.259 0.023 -0.034 -0.235 0.233 -0.239 0.096 0.136 -0.750 0.659 -0.485 0.370 -0.173 0.935 -0.771 -0.057 -0.104 0.073 -0.694 -0.233 -0.409 0.098
W3 (output layer weights), shape 32 x 5: one line per row 0.001 -0.130 -0.777 0.023 -0.054 0.451 0.607 0.607 0.489 0.489 0.133 1.003 1.898 0.059 0.046 -0.086 -0.447 0.660 -0.054 0.017 0.546 -0.730 0.012 0.408 0.482 -0.027 -1.098 -0.804 0.004 -0.080 -1.023 0.412 -1.348 -1.165 -1.053 -0.175 0.243 -0.522 -0.175 -0.220 0.618 1.223 -0.308 0.359 0.471 -0.382 0.294 -1.229 -0.428 -0.371 0.505 -0.724 0.101 0.432 0.397 -0.110 0.120 -0.656 -0.189 -0.141 0.775 0.725 1.534 0.715 0.801 -0.258 0.225 0.396 -0.223 -0.337 0.552 -0.623 1.020 0.827 0.696 -0.335 0.122 -0.202 -0.287 -0.244 -0.056 1.178 1.165 -0.032 -0.068 0.684 1.549 -0.541 1.095 0.970 -1.399 -1.370 1.587 -1.283 -1.215 0.176 1.388 1.367 0.422 0.479 -0.231 -1.075 0.356 -0.212 -0.068 0.510 1.366 -0.376 0.430 0.582 0.725 0.779 1.654 0.691 0.737 1.286 1.734 -0.953 1.081 1.053 -1.225 0.580 -1.404 -0.921 -1.282 -0.167 -0.895 0.502 -0.104 -0.123 0.045 0.289 -0.820 0.017 0.068 -0.651 -0.836 0.202 -0.584 -0.546 -1.100 0.407 -1.500 -1.169 -1.028 0.056 -0.690 -0.814 0.015 0.029 -0.271 -0.297 0.352 -0.306 -0.307 0.553 1.950 0.123 0.616 0.587
b3 (output layer biases), shape 5: 0.931 1.261 1.065 0.861 0.855
(1,1), weights include 2.697, -2.474, and -2.322; at (2,1), they include -3.588 and 2.245. This suggests a player-controlled object confined to the left side—a vertical paddle.(1,2), examples are -3.169, 2.039, and -3.022; at (1,5), 2.305 and 3.278. This fits a ball travelling through the court, not an agent navigating it.(1,1), the first four are 0.073, 0.015, -0.076, 0.000. There is instead a repeated, bias-like pattern on the right boundary: at (1,9), weights begin 0.028, -0.052, 0.203, 0.316. This suggests a fixed scoring/target boundary, not scattered food or a field of destructible bricks.(0,0) includes 0.275 toward hidden unit 3 and 0.289 toward unit 20; the corresponding biases are 0.271 and 0.248. Always-present walls can act as additional biases; this is not evidence of learned maze navigation.(1,1), examples include -1.578 and 1.031; at (1,5), they reach 5.236 and 3.913. Previous positions evidently matter, consistent with estimating ball direction and predicting interception.[-1.399, -1.370, 1.587, -1.283, -1.215], strongly distinguishing down; row 31 is [0.553, 1.950, 0.123, 0.616, 0.587], distinguishing up. Biases [0.931, 1.261, 1.065, 0.861, 0.855] mildly favour up, but do not alone establish action frequencies. The three similar columns suggest three effectively equivalent “stay” actions.A left-edge paddle, vertically meaningful controls, an independently moving ball, and substantial use of previous positions strongly identify Pong-style interception.
Ordinary Breakout would usually have a bottom paddle, meaningful left/right controls, and structured brick-target inputs. Snake, maze navigation, and food collection would require agent-position structure across both dimensions and generally distinguish all four directions. A simple falling-object catcher likewise usually moves horizontally.
The fixed right-side target pattern and lack of an evident independently moving opponent favour a single-paddle, wall-return variant, although the exact scoring rules cannot be established from these weights alone.
Pong — most likely a single-player, vertical-paddle wall-Pong variant.
The agent moves a paddle up and down along the left side to intercept and return a moving ball, avoiding misses. Reward likely comes from successful returns and/or the ball reaching the opposite scoring boundary.
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