Hockey self-play from coordinates

Both agents move up, stay, or move down. The puck bounces off the top and bottom walls. Returns accelerate its horizontal speed by 7%, up to 0.055 field widths per move. Where the puck hits a paddle changes the return angle; moving the paddle adds a small spin effect.

A paddle moves 0.035 field heights per decision. The puck’s vertical speed can reach 0.055, so just following its current height can be too slow. Anticipating rebounds and choosing a return position matter. Paddles cover 26% of the goal’s height; their centers stay inside the rink.

A point is scored when the puck crosses the opponent’s paddle line without being returned. Serves alternate direction and randomize the puck and paddle positions. A rally that lasts 400 moves becomes a draw to prevent indefinite loops. This is a simplified, discrete Pong-style game with hockey visuals, not a full rigid-body physics simulation.

Each agent sees seven numbers: its own paddle height, the opponent’s height, puck x and y, horizontal and vertical puck velocity, and the signed vertical gap between itself and the puck. Positions are centered around zero, and velocities are divided by 0.055.

Violet’s horizontal coordinates and horizontal velocity are mirrored, so both agents reason from the same orientation: their own goal is on the left. Neither gets the other agent’s intended move.

Both agents have their own feedforward network, target network, and replay memory. Each hidden layer has 16 tanh neurons. The three linear outputs estimate rewards for up, stay, and down. Choose one to four hidden layers independently for each agent.

With input count I, hidden width h, and depth d, the trainable parameter count is:

The one-layer coordinate network has 179 parameters. Extra layers add capacity, computation, and more derivative factors. The layer-gradient readout shows whether useful update signals are reaching the earliest layers.

Changing one depth resets both agents. Start with 1 versus 1, then try 1 versus 3 using the same seed. Swap which side gets the deeper network and repeat with another seed. More parameters do not guarantee a stronger player. Equal moves give equal experience counts, not equal computation or identical experience.

First-layer weights start uniformly in [−0.5, 0.5], output weights in [−0.35, 0.35], and intervening hidden weights in ±√(3/h). That last range has standard deviation 1/√h, the fan-in scaling LeCun and colleagues recommended for normalized inputs and a scaled tanh (LeCun et al., 1998, §4.6). Biases start at zero. The network depth lesson compares initialization schemes and depths on a simpler regression problem. No pretrained policy, scripted opponent, or correct-action labels are used during self-play.

Scoring earns +1 for the winner and −1 for the defender. Earlier decisions also receive coaching: while the puck approaches an agent, reducing the gap to its current height earns twice that reduction. A successful return adds +0.25.

Here p and p′ are the paddle positions before and after its move, with the puck height held fixed. When a goal occurs, the ±1 result replaces coaching rewards for that move. Draws do not award a goal bonus. The match score is competitive; the extra training rewards are not zero-sum and deliberately help the agents learn basic defense.

Both networks choose actions from the same pre-move state. Then the environment advances once and gives each agent its own reward and next observation. Agents use epsilon-greedy exploration: a fixed chance of selecting a random move instead of their highest-valued action.

Every move creates one experience per agent. Each replay buffer holds up to 1,500 transitions. After 32 moves, each agent samples eight transitions with replacement and takes eight sequential updates per game move.

The target copy is refreshed every 120 training moves. The two agents never share weights, targets, replay samples, or gradients. The loss display smooths the average of the eight sampled losses; the gradient bars describe only the last sampled update and exclude biases.

This is independent Q-learning against another changing learner: each agent treats the other as part of its environment, the simplest way to extend deep Q-learning to several agents (Tampuu et al., 2017). Old replay experiences can become less representative as the opponent improves, so training may oscillate or favor one side. A low loss or a temporary lead does not prove a robust strategy. The basic replay-and-target method is explained in the PyTorch DQN tutorial, which updates its target network gradually instead of copying it (PyTorch DQN tutorial, n.d.). The Catch a star history covers where replay and target networks come from.

Watch mode freezes both networks, disables random exploration, and keeps a separate scoreboard. Return rate measures successful returns per incoming shot; average rally length measures the number of returns before a goal or draw. A perfectly even score can conceal two poor policies or two strong ones.

Training charts always span rally 0 to the present. Older checkpoints are thinned to bound memory while retaining the beginning. Return-rate checkpoints use up to 100 recent incoming shots; the agent cards show the cumulative rate for the active match. A larger speed setting processes more moves per second, up to the device’s available capacity.

Reset and choose settings before comparing runs. Switching into watch mode consumes new serves and changes the subsequent training trajectory if you resume. These experiments demonstrate competitive learning in one small, coached environment; they do not establish general game-playing ability.

import { randomSource } from './star-training';
import { HockeyNetwork } from './hockey-network';

export const HOCKEY_ACTIONS = ['Up', 'Stay', 'Down'];
export const RINK_SIZE = 12;
export const PADDLE_HALF = .13;
export const PUCK_RADIUS = .018;
export type HockeyMode = 'state' | 'pixels';
export type Side = 0 | 1;
export type HockeySettings = { rates: [number, number]; exploration: [number, number] };
export const HOCKEY_DEFAULTS: HockeySettings = { rates: [.01, .01], exploration: [.2, .2] };
export type Rink = { paddles: [number, number]; x: number; y: number; vx: number; vy: number };
type Transition = { state: number[]; action: number; reward: number; next: number[]; done: boolean };
export type MatchStats = { rallies: number; goals: [number, number]; returns: [number, number]; shots: [number, number]; draws: number; recent: [number[], number[]]; lengths: number[] };
const stats = (): MatchStats => ({ rallies: 0, goals: [0, 0], returns: [0, 0], shots: [0, 0], draws: 0, recent: [[], []], lengths: [] });
export const createMatchStats = stats;

export function rinkPixels(rink: Rink, side: Side): number[] {
  const frame = Array(RINK_SIZE * RINK_SIZE).fill(0) as number[];
  const own = rink.paddles[side], other = rink.paddles[1 - side];
  for (let row = 0; row < RINK_SIZE; row++) {
    for (const [column, paddle] of [[0, own], [RINK_SIZE - 1, other]]) {
      const center = paddle * (RINK_SIZE - 1), half = PADDLE_HALF * (RINK_SIZE - 1);
      const coverage = Math.max(0, Math.min(row + .5, center + half) - Math.max(row - .5, center - half));
      frame[row * RINK_SIZE + column] = .7 * coverage;
    }
  }
  const x = (side === 0 ? rink.x : 1 - rink.x) * (RINK_SIZE - 1), y = rink.y * (RINK_SIZE - 1);
  const left = Math.floor(x), top = Math.floor(y);
  for (let dy = 0; dy <= 1; dy++) for (let dx = 0; dx <= 1; dx++) {
    const column = left + dx, row = top + dy;
    if (column >= 0 && column < RINK_SIZE && row >= 0 && row < RINK_SIZE) {
      const brightness = (dx ? x - left : 1 - (x - left)) * (dy ? y - top : 1 - (y - top));
      frame[row * RINK_SIZE + column] = Math.max(frame[row * RINK_SIZE + column], brightness);
    }
  }
  return frame;
}

export class HockeyAgent {
  network: HockeyNetwork;
  target: HockeyNetwork;
  memory: Transition[] = [];
  cursor = 0;
  steps = 0;
  loss = 0;
  action = 1;
  exploring = false;
  values = [0, 0, 0];
  lastInput: number[];
  random: () => number;
  constructor(mode: HockeyMode, seed: number, depth = 1, public options = { memoryLimit: 1500, replaySamples: 8, width: 0 }) {
    this.random = randomSource(seed);
    const inputs = mode === 'pixels' ? 2 * RINK_SIZE ** 2 : 7;
    const hidden = options.width || (mode === 'pixels' ? 24 : 16);
    this.network = new HockeyNetwork(this.random, inputs, hidden, depth);
    this.target = new HockeyNetwork(this.random, inputs, hidden, depth);
    this.target.copyFrom(this.network);
    this.lastInput = Array(inputs).fill(0);
  }
  choose(state: number[], epsilon: number) {
    this.lastInput = state;
    this.values = this.network.forward(state).values;
    this.exploring = epsilon > 0 && this.random() < epsilon;
    this.action = this.exploring ? Math.floor(this.random() * 3) : this.values.indexOf(Math.max(...this.values));
    return this.action;
  }
  learn(transition: Transition, rate: number) {
    this.steps++;
    if (this.memory.length < this.options.memoryLimit) this.memory.push(transition);
    else { this.memory[this.cursor] = transition; this.cursor = (this.cursor + 1) % this.options.memoryLimit; }
    if (this.memory.length >= 32) {
      let loss = 0;
      for (let i = 0; i < this.options.replaySamples; i++) {
        const sample = this.memory[Math.floor(this.random() * this.memory.length)];
        const target = sample.reward + (sample.done ? 0 : .95 * Math.max(...this.target.forward(sample.next).values));
        loss += this.network.train(sample.state, sample.action, target, rate / 4);
      }
      this.loss = .95 * this.loss + .05 * loss / this.options.replaySamples;
    }
    if (this.steps % 120 === 0) this.target.copyFrom(this.network);
  }
}

export type HockeyCheckpoint = { rally: number; rates: [number, number]; averageLength: number; goals: [number, number] };
export class HockeyMatch {
  agents: [HockeyAgent, HockeyAgent];
  rink: Rink = { paddles: [.5, .5], x: .5, y: .5, vx: .028, vy: .01 };
  random: () => number;
  previous: [number[], number[]];
  training = stats();
  watching = stats();
  history: HockeyCheckpoint[] = [];
  lastCompleted: HockeyCheckpoint;
  stride = 5;
  tick = 0;
  terminal = false;
  winner: Side | null = null;
  hits = 0;
  rewards: [number, number] = [0, 0];
  serves = 0;
  constructor(public mode: HockeyMode = 'state', seed = 42, public depths: [number, number] = [1, 1]) {
    this.random = randomSource(seed);
    this.agents = [new HockeyAgent(mode, seed + 11, depths[0]), new HockeyAgent(mode, seed + 29, depths[1])];
    this.previous = [[], []];
    this.newRally();
    this.agents.forEach((agent, side) => { agent.lastInput = this.observation(side as Side); });
    this.lastCompleted = this.checkpoint();
    this.history.push(this.lastCompleted);
  }
  newRally() {
    this.rink = { paddles: [.3 + .4 * this.random(), .3 + .4 * this.random()], x: .5, y: .2 + .6 * this.random(), vx: (this.serves++ % 2 === 0 ? 1 : -1) * .028, vy: (this.random() * 2 - 1) * .018 };
    this.tick = 0; this.terminal = false; this.winner = null; this.hits = 0; this.rewards = [0, 0];
    this.previous = [rinkPixels(this.rink, 0), rinkPixels(this.rink, 1)];
  }
  startWatch() { this.watching = stats(); this.newRally(); }
  observation(side: Side) {
    if (this.mode === 'pixels') return [...rinkPixels(this.rink, side), ...this.previous[side]];
    const { x, y, vx, vy, paddles } = this.rink;
    return [2 * paddles[side] - 1, 2 * paddles[1 - side] - 1, 2 * (side === 0 ? x : 1 - x) - 1, 2 * y - 1, (side === 0 ? vx : -vx) / .055, vy / .055, y - paddles[side]];
  }
  advance(actions: [number, number], matchStats: MatchStats) {
    const rink = this.rink;
    const approaching: Side = rink.vx < 0 ? 0 : 1;
    const before = Math.abs(rink.y - rink.paddles[approaching]);
    rink.paddles = rink.paddles.map((paddle, side) => Math.max(PADDLE_HALF, Math.min(1 - PADDLE_HALF, paddle + (actions[side] - 1) * .035))) as [number, number];
    this.rewards = [0, 0];
    // Coaching depends only on movement toward the current puck height, before
    // the puck moves. It is supplied by the environment, not as an action label.
    this.rewards[approaching] = 2 * (before - Math.abs(rink.y - rink.paddles[approaching]));
    const oldX = rink.x;
    rink.x += rink.vx; rink.y += rink.vy;
    if (rink.y < PUCK_RADIUS) { rink.y = 2 * PUCK_RADIUS - rink.y; rink.vy = Math.abs(rink.vy); }
    if (rink.y > 1 - PUCK_RADIUS) { rink.y = 2 * (1 - PUCK_RADIUS) - rink.y; rink.vy = -Math.abs(rink.vy); }
    const defender: Side | null = rink.vx < 0 && oldX > .065 && rink.x <= .065 ? 0 : rink.vx > 0 && oldX < .935 && rink.x >= .935 ? 1 : null;
    if (defender !== null) {
      const returned = Math.abs(rink.y - rink.paddles[defender]) <= PADDLE_HALF + PUCK_RADIUS;
      matchStats.shots[defender]++;
      matchStats.recent[defender].push(Number(returned));
      if (matchStats.recent[defender].length > 100) matchStats.recent[defender].shift();
      if (returned) {
        matchStats.returns[defender]++; this.hits++; this.rewards[defender] += .25;
        const offset = (rink.y - rink.paddles[defender]) / (PADDLE_HALF + PUCK_RADIUS);
        rink.vx = (defender === 0 ? 1 : -1) * Math.min(.055, Math.abs(rink.vx) * 1.07);
        rink.vy = offset * .05 + (actions[defender] - 1) * .005;
        rink.x = defender === 0 ? .065 + (.065 - rink.x) : .935 - (rink.x - .935);
      } else {
        this.terminal = true; this.winner = (1 - defender) as Side;
        this.rewards[this.winner] = 1; this.rewards[defender] = -1;
        matchStats.goals[this.winner]++;
      }
    }
    this.tick++;
    if (!this.terminal && this.tick >= 400) { this.terminal = true; matchStats.draws++; }
    if (this.terminal) {
      matchStats.rallies++; matchStats.lengths.push(this.hits);
      if (matchStats.lengths.length > 100) matchStats.lengths.shift();
    }
  }
  step(settings: HockeySettings, train = true) {
    if (this.terminal) this.newRally();
    const states = [this.observation(0), this.observation(1)];
    // Both decisions observe the same pre-move state. Neither sees the other's
    // action before selecting its own.
    const actions = this.agents.map((agent, side) => agent.choose(states[side], train ? settings.exploration[side] : 0)) as [number, number];
    const frames = [rinkPixels(this.rink, 0), rinkPixels(this.rink, 1)] as [number[], number[]];
    this.advance(actions, train ? this.training : this.watching);
    this.previous = frames;
    if (train) {
      this.agents.forEach((agent, side) => agent.learn({ state: states[side], action: actions[side], reward: this.rewards[side], next: this.observation(side as Side), done: this.terminal }, settings.rates[side]));
      if (this.terminal) this.lastCompleted = this.checkpoint();
      if (this.terminal && this.training.rallies % this.stride === 0) {
        this.history.push(this.lastCompleted);
        if (this.history.length > 201) { this.history = this.history.filter((_, i) => i % 2 === 0); this.stride *= 2; }
      }
    }
  }
  checkpoint(): HockeyCheckpoint {
    const rates = this.training.recent.map(shots => shots.length ? shots.reduce((sum, hit) => sum + hit, 0) / shots.length : NaN) as [number, number];
    const lengths = this.training.lengths;
    return { rally: this.training.rallies, rates, averageLength: lengths.length ? lengths.reduce((a, b) => a + b, 0) / lengths.length : 0, goals: [...this.training.goals] };
  }
}

type DenseLayer = { weights: number[][]; biases: number[] };

// Configurable-depth Q-network: tanh hidden layers, three linear action values.
export class HockeyNetwork {
  layers: DenseLayer[];
  gradientRms: number[];
  constructor(random: () => number, inputs: number, width: number, depth: number) {
    const sizes = [inputs, ...Array(depth).fill(width), 3];
    this.layers = sizes.slice(1).map((outputs, l) => {
      const bound = l === 0 ? .5 : l === depth ? .35 : Math.sqrt(3 / width);
      return { weights: Array.from({ length: outputs }, () => Array.from({ length: sizes[l] }, () => (random() * 2 - 1) * bound)), biases: Array(outputs).fill(0) };
    });
    this.gradientRms = this.layers.map(() => 0);
  }
  get parameterCount() { return this.layers.reduce((sum, layer) => sum + layer.weights.length * layer.weights[0].length + layer.biases.length, 0); }
  forward(input: number[]) {
    const activations = [input];
    this.layers.forEach((layer, l) => {
      activations.push(layer.weights.map((row, j) => {
        const z = row.reduce((sum, w, i) => sum + w * activations[l][i], layer.biases[j]);
        return l === this.layers.length - 1 ? z : Math.tanh(z);
      }));
    });
    return { values: activations.at(-1)!, activations };
  }
  copyFrom(other: HockeyNetwork) {
    this.layers = other.layers.map(layer => ({ weights: layer.weights.map(row => [...row]), biases: [...layer.biases] }));
  }
  train(input: number[], action: number, target: number, rate: number) {
    const { activations, values } = this.forward(input);
    const error = values[action] - target;
    let delta = values.map((_, i) => i === action ? Math.max(-1, Math.min(1, error)) : 0);
    for (let l = this.layers.length - 1; l >= 0; l--) {
      const layer = this.layers[l];
      // Propagate with the old weights before applying this layer's update.
      const previous = l > 0 ? activations[l].map((a, i) => layer.weights.reduce((sum, row, j) => sum + row[i] * delta[j], 0) * (1 - a * a)) : [];
      let squares = 0, count = 0;
      layer.weights.forEach((row, j) => {
        row.forEach((w, i) => { const gradient = delta[j] * activations[l][i]; squares += gradient * gradient; count++; row[i] = w - rate * gradient; });
        layer.biases[j] -= rate * delta[j];
      });
      this.gradientRms[l] = Math.sqrt(squares / count);
      delta = previous;
    }
    return Math.abs(error) <= 1 ? .5 * error ** 2 : Math.abs(error) - .5;
  }
}