Add Ants arena (fog-of-war swarm RTS)

Thin arena wrapper cloning CodeClash-ai/Ants, replay renderer (spectator view of
ants/hills/food/water, per-sim winner via peek_winner), registration, and test config.

Author: john-b-yang

codeclash/arenas/__init__.py

@@ -1,4 +1,5 @@
 from codeclash.arenas.arena import CodeArena
+from codeclash.arenas.ants.ants import AntsArena
 from codeclash.arenas.battlecode23.battlecode23 import BattleCode23Arena
 from codeclash.arenas.battlecode24.battlecode24 import BattleCode24Arena
 from codeclash.arenas.battlecode25.battlecode25 import BattleCode25Arena
@@ -21,6 +22,7 @@
 from codeclash.arenas.scml.scml import SCMLOneShotArena
 
 ARENAS = [
+    AntsArena,
     BattleCode23Arena,
     BattleCode24Arena,
     BattleCode25Arena,

codeclash/arenas/ants/Ants.Dockerfile

@@ -0,0 +1,18 @@
+FROM ubuntu:22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install Python 3.10, pip, and prerequisites
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends \
+    curl ca-certificates python3.10 python3.10-venv \
+    python3-pip python-is-python3 wget git build-essential jq curl locales \
+ && rm -rf /var/lib/apt/lists/*
+
+# Clone Ants game repository
+RUN git clone https://github.com/CodeClash-ai/Ants.git /workspace \
+    && cd /workspace \
+    && git remote set-url origin https://github.com/CodeClash-ai/Ants.git
+WORKDIR /workspace
+
+# No additional dependencies needed - engine uses only the standard library

codeclash/arenas/ants/__init__.py

@@ -0,0 +1,92 @@
+import re
+
+from codeclash.agents.player import Player
+from codeclash.arenas.arena import CodeArena, RoundStats
+from codeclash.constants import RESULT_TIE
+from codeclash.utils.environment import assert_zero_exit_code
+
+ANTS_LOG = "result.log"
+
+
+class AntsArena(CodeArena):
+    name: str = "Ants"
+    submission: str = "main.py"
+    description: str = """Your bot (`main.py`) commands a swarm of ants in Ants, a fog-of-war RTS on a toroidal
+(wrap-around) grid, in the spirit of the 2010 Ants AI Challenge. Every turn all ants move one cell at
+once. You only see the map within your ants' view radius (fog of war). Combat is focus-fire: an ant dies
+unless it has strictly fewer enemies within attack range than every enemy attacking it (so gang up to
+win fights). Food within spawn radius of exactly one player's ants is gathered and spawns a new ant on a
+hill. Move an ant onto an enemy hill to raze it -- razing enemy hills is the objective. Most hills razed
+(tiebreak: ants alive) wins.
+
+Your bot must implement:
+    def do_turn(obs: dict) -> list
+
+Return a list of moves, each [row, col, dir] with dir in "N","S","E","W" (N = row-1); the ant at
+(row, col) steps that way. Un-ordered ants stay; moving into water is ignored; two ants onto one cell
+both die. do_turn runs in one long-lived process, so you may keep state (a remembered map) in globals.
+
+`obs` is your fog-limited view: obs["turn"]/["max_turns"], obs["rows"]/["cols"] (toroidal),
+obs["viewradius2"]/["attackradius2"]/["spawnradius2"], obs["you"], obs["my_ants"], obs["my_hills"],
+and (only where visible) obs["enemy_ants"], obs["enemy_hills"], obs["food"], obs["water"].
+Distances are toroidal: dr=min(|dr|,rows-|dr|), dc likewise, compare dr*dr+dc*dc to the radius2 values.
+"""
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, **kwargs)
+        assert len(config["players"]) >= 2, "Ants needs at least two players"
+
+    def execute_round(self, agents: list[Player]) -> None:
+        args = [f"/{agent.name}/{self.submission}" for agent in agents]
+        cmd = (
+            f"python engine.py {' '.join(args)} -r {self.game_config['sims_per_round']} "
+            f"-o {self.log_env} > {self.log_env / ANTS_LOG};"
+        )
+        self.logger.info(f"Running game: {cmd}")
+        assert_zero_exit_code(self.environment.execute(cmd))
+
+    def get_results(self, agents: list[Player], round_num: int, stats: RoundStats):
+        with open(self.log_round(round_num) / ANTS_LOG) as f:
+            round_log = f.read()
+        lines = round_log.split("FINAL_RESULTS")[-1].splitlines()
+
+        # Engine prints: "Bot_<i>: <wins> games won (<name>)". Score = games won.
+        scores: dict[str, float] = {}
+        for line in lines:
+            match = re.search(r"Bot_(\d+):\s+(\d+)\s+games\s+won", line)
+            if match:
+                bot_id = int(match.group(1))
+                wins = int(match.group(2))
+                if 1 <= bot_id <= len(agents):
+                    scores[agents[bot_id - 1].name] = wins
+
+        draw_match = re.search(r"Draws:\s+(\d+)", round_log)
+        if draw_match and int(draw_match.group(1)) > 0:
+            scores[RESULT_TIE] = int(draw_match.group(1))
+
+        if scores:
+            real = {k: v for k, v in scores.items() if k != RESULT_TIE}
+            max_score = max(real.values()) if real else 0
+            winners = [k for k, v in real.items() if v == max_score]
+            stats.winner = winners[0] if len(winners) == 1 else RESULT_TIE
+        else:
+            stats.winner = "unknown"
+
+        stats.scores = scores
+        for player, score in scores.items():
+            if player != RESULT_TIE:
+                stats.player_stats[player].score = score
+
+    def validate_code(self, agent: Player) -> tuple[bool, str | None]:
+        if self.submission not in agent.environment.execute("ls")["output"]:
+            return False, f"No {self.submission} file found in the root directory"
+
+        bot_content = agent.environment.execute(f"cat {self.submission}")["output"]
+        if "def do_turn(" not in bot_content:
+            return (
+                False,
+                f"{self.submission} must define a do_turn(obs) function. "
+                "See the game description for the required signature.",
+            )
+
+        return True, None

codeclash/arenas/ants/ants.py

@@ -0,0 +1,138 @@
+"""Ants replay renderer.
+
+Parses a per-game ``sim_*.json`` written by the engine and renders the toroidal grid
+from the spectator's view (no fog): water, food, hills, and every player's ants.
+
+The JSON format (see engine.py ``write_replay``)::
+
+    {"rows":32, "cols":32, "num_players":2, "water":[[r,c], ...],
+     "names":["p1","p2"], "winner": 0 | null,
+     "frames":[{"t":0, "ants":[[r,c,owner], ...], "hills":[[r,c,owner], ...],
+                "food":[[r,c], ...]}, ...]}
+
+Water is static (recorded once). Each frame lists the live ants, living hills, and
+food. ``winner`` is the winning player id, or ``null`` for a draw. Grid cells are
+``[row, col]`` (row = y, col = x); the board wraps, but the renderer just draws it flat.
+"""
+
+from __future__ import annotations
+
+import json
+
+from codeclash.replay.base import ReplayData, ReplayRenderer
+
+DRAW_JS = """
+const ARENA = (function(){
+  const PAL = ['#e5484d','#4593ff','#46a758','#f5d90a','#8e4ec6','#f76b15','#e93d82','#12a594'];
+  const BG = '#0d1117', WATER = '#15304a', FOOD = '#e6edf3', LINE = 'rgba(255,255,255,0.04)';
+  let COLS, ROWS, CELL, WATERSET;
+  function col(i){ return PAL[i % PAL.length]; }
+  function setup(cv, G){
+    COLS = G.w; ROWS = G.h;
+    CELL = Math.max(8, Math.min(22, Math.floor(640 / COLS)));
+    cv.width = COLS * CELL; cv.height = ROWS * CELL;
+    WATERSET = G.water || [];
+  }
+  function draw(ctx, cv, G, i){
+    const f = G.frames[i];
+    ctx.fillStyle = BG; ctx.fillRect(0, 0, cv.width, cv.height);
+    // water
+    ctx.fillStyle = WATER;
+    WATERSET.forEach(w => ctx.fillRect(w[1]*CELL, w[0]*CELL, CELL, CELL));
+    // grid lines
+    ctx.strokeStyle = LINE; ctx.lineWidth = 1;
+    for(let x=0;x<=COLS;x++){ ctx.beginPath(); ctx.moveTo(x*CELL,0); ctx.lineTo(x*CELL,cv.height); ctx.stroke(); }
+    for(let y=0;y<=ROWS;y++){ ctx.beginPath(); ctx.moveTo(0,y*CELL); ctx.lineTo(cv.width,y*CELL); ctx.stroke(); }
+    // hills (large bordered square in the owner's color)
+    (f.hills||[]).forEach(h => {
+      const x = h[1]*CELL, y = h[0]*CELL;
+      ctx.fillStyle = col(h[2]); ctx.globalAlpha = 0.35; ctx.fillRect(x, y, CELL, CELL); ctx.globalAlpha = 1;
+      ctx.strokeStyle = col(h[2]); ctx.lineWidth = 2; ctx.strokeRect(x+1.5, y+1.5, CELL-3, CELL-3);
+    });
+    // food (small white diamonds)
+    ctx.fillStyle = FOOD;
+    (f.food||[]).forEach(fd => {
+      const cx = fd[1]*CELL + CELL/2, cy = fd[0]*CELL + CELL/2, s = Math.max(2, CELL*0.22);
+      ctx.beginPath(); ctx.moveTo(cx, cy-s); ctx.lineTo(cx+s, cy); ctx.lineTo(cx, cy+s); ctx.lineTo(cx-s, cy); ctx.closePath(); ctx.fill();
+    });
+    // ants (filled rounded cells in the owner's color)
+    (f.ants||[]).forEach(a => {
+      ctx.fillStyle = col(a[2]);
+      const x = a[1]*CELL, y = a[0]*CELL, p = Math.max(1, CELL*0.12);
+      ctx.fillRect(x+p, y+p, CELL-2*p, CELL-2*p);
+    });
+  }
+  function side(G, i){
+    const f = G.frames[i], NM = G.names || [], n = G.num_players || NM.length;
+    const done = i === G.frames.length - 1;
+    const ants = {}, hills = {};
+    (f.ants||[]).forEach(a => ants[a[2]] = (ants[a[2]]||0) + 1);
+    (f.hills||[]).forEach(h => hills[h[2]] = (hills[h[2]]||0) + 1);
+    let rows = '';
+    for(let p=0;p<n;p++){
+      const dead = (ants[p]||0) === 0 && (hills[p]||0) === 0;
+      rows += `<div class="team ${dead?'tdead':''}"><div class="tname">`
+        + `<span class="sw" style="background:${col(p)}"></span>${NM[p] || ('player'+(p+1))}</div>`
+        + `<div class="stat"><span>ants</span><b>${ants[p]||0}</b></div>`
+        + `<div class="stat"><span>hills</span><b>${hills[p]||0}</b></div></div>`;
+    }
+    const res = done ? (G.draw ? 'DRAW' : (G.winner || '\\u2014')) : '';
+    return rows
+      + `<div class="stat"><span>turn</span><b>${f.turn} / ${G.max_turns||''}</b></div>`
+      + (done ? `<div class="stat"><span>result</span><b>${res}</b></div>` : '');
+  }
+  return {setup, draw, side};
+})();
+"""
+
+
+class AntsReplayer(ReplayRenderer):
+    arena = "Ants"
+    sim_glob = "sim_*.json"
+    DRAW_JS = DRAW_JS
+
+    def parse(self, raw: bytes, players: list[dict] | None = None) -> ReplayData:
+        log = json.loads(raw.decode(errors="replace"))
+        rows = log.get("rows", 32)
+        cols = log.get("cols", 32)
+        n = log.get("num_players", 2)
+
+        names = list(log.get("names", []))
+        if players:
+            names = [p.get("name", names[i] if i < len(names) else f"player{i + 1}") for i, p in enumerate(players)]
+        while len(names) < n:
+            names.append(f"player{len(names) + 1}")
+
+        frames = [
+            {"turn": fr.get("t", idx), "ants": fr.get("ants", []), "hills": fr.get("hills", []), "food": fr.get("food", [])}
+            for idx, fr in enumerate(log.get("frames", []))
+        ]
+
+        win = log.get("winner")
+        draw = win is None
+        winner = None if draw else names[win] if isinstance(win, int) and 0 <= win < len(names) else str(win)
+
+        return ReplayData(
+            w=cols,
+            h=rows,
+            frames=frames,
+            winner=winner,
+            draw=draw,
+            extra={
+                "names": names,
+                "num_players": n,
+                "max_turns": log.get("max_turns", 500),
+                "water": log.get("water", []),
+            },
+        )
+
+    def peek_winner(self, raw: bytes, players: list[dict] | None = None) -> tuple[str | None, bool] | None:
+        log = json.loads(raw.decode(errors="replace"))
+        win = log.get("winner")
+        if win is None:
+            return (None, True)
+        names = list(log.get("names", []))
+        if players:
+            names = [p.get("name", names[i] if i < len(names) else f"player{i + 1}") for i, p in enumerate(players)]
+        name = names[win] if isinstance(win, int) and 0 <= win < len(names) else str(win)
+        return (name, False)

codeclash/arenas/ants/replay.py

@@ -61,6 +61,10 @@ def get_replayer(arena: str) -> ReplayRenderer | None:
         from codeclash.arenas.paintvolley.replay import PaintVolleyReplayer
 
         return PaintVolleyReplayer()
+    if arena == "Ants":
+        from codeclash.arenas.ants.replay import AntsReplayer
+
+        return AntsReplayer()
     if arena == "Halite":
         from codeclash.arenas.halite.replay import HaliteReplayer
 

codeclash/replay/__init__.py

@@ -0,0 +1,34 @@
+tournament:
+  rounds: 3
+game:
+  name: Ants
+  sims_per_round: 6
+players:
+- agent: dummy
+  name: p1
+- agent: dummy
+  name: p2
+prompts:
+  game_description: |
+    You are a software developer ({{player_id}}) competing in a coding game called Ants.
+    Your bot (`main.py`) commands a swarm of ants in a fog-of-war RTS on a toroidal (wrap-around) grid,
+    in the spirit of the 2010 Ants AI Challenge. Every turn all ants move one cell at once. You only see
+    the map within your ants' view radius. Combat is focus-fire: an ant dies unless it has strictly fewer
+    enemies within attack range than every enemy attacking it (gang up to win fights). Food within spawn
+    radius of exactly one player's ants is gathered and spawns a new ant on a hill. Move an ant onto an
+    enemy hill to raze it -- razing enemy hills is the objective. Most hills razed (tiebreak: ants alive)
+    wins.
+
+    Your bot must implement `do_turn(obs) -> list`, returning moves [row, col, dir] with dir in
+    "N","S","E","W". Un-ordered ants stay; moving into water is ignored; two ants onto one cell both die.
+    do_turn runs in one long-lived process, so you may remember the map in module globals. `obs` is your
+    fog-limited view: obs["turn"]/["max_turns"], obs["rows"]/["cols"] (toroidal),
+    obs["viewradius2"]/["attackradius2"]/["spawnradius2"], obs["you"], obs["my_ants"], obs["my_hills"],
+    and (only where visible) obs["enemy_ants"], obs["enemy_hills"], obs["food"], obs["water"].
+
+    The game is played in {{rounds}} rounds. For every round, you (and your competitor) edit program
+    code that controls your bot. This is round {{round}}.
+    After you and your competitor finish editing your codebases, the game is run automatically.
+
+    Your task: improve the bot in `main.py`, located in {{working_dir}}.
+    {{working_dir}} is your codebase, which contains both your bot and supporting assets.