Add JAX Lost Cities rules engine

pyproject.toml

@@ -13,6 +13,8 @@ dependencies = [
     "pydantic>=2.7",
     "PyYAML>=6.0",
     "torch>=2.3.0",
+    "jax>=0.10.2",
+    "chex>=0.1.92",
 ]
 
 [project.optional-dependencies]
@@ -45,7 +47,7 @@ build-backend = "setuptools.build_meta"
 
 [tool.setuptools.packages.find]
 where = ["src"]
-include = ["coolrl_lost_cities*"]
+include = ["coolrl_lost_cities*", "lost_cities_jax*"]
 
 [tool.setuptools.package-data]
 "coolrl_lost_cities.games.classic" = [

src/lost_cities_jax/__init__.py

@@ -0,0 +1,47 @@
+"""JAX Lost Cities rules engine."""
+
+from lost_cities_jax.batched import (
+    batched_legal_mask,
+    batched_obs,
+    batched_reset,
+    batched_reset_from_order,
+    batched_step,
+)
+from lost_cities_jax.engine import (
+    board_score,
+    current_hand_sorted,
+    decode_action,
+    legal_action_mask,
+    reset,
+    reset_from_order,
+    score,
+    step,
+)
+from lost_cities_jax.obs import observation
+from lost_cities_jax.types import (
+    MAX_STEPS,
+    N_ACTIONS,
+    OBS_DIM,
+    State,
+)
+
+__all__ = [
+    "MAX_STEPS",
+    "N_ACTIONS",
+    "OBS_DIM",
+    "State",
+    "batched_legal_mask",
+    "batched_obs",
+    "batched_reset",
+    "batched_reset_from_order",
+    "batched_step",
+    "board_score",
+    "current_hand_sorted",
+    "decode_action",
+    "legal_action_mask",
+    "observation",
+    "reset",
+    "reset_from_order",
+    "score",
+    "step",
+]

src/lost_cities_jax/batched.py

@@ -0,0 +1,22 @@
+"""Jitted vmap wrappers for the Lost Cities JAX engine."""
+
+from __future__ import annotations
+
+import jax
+
+from lost_cities_jax.engine import legal_action_mask, reset, reset_from_order, step
+from lost_cities_jax.obs import observation
+
+batched_reset = jax.jit(jax.vmap(reset))
+batched_reset_from_order = jax.jit(jax.vmap(reset_from_order))
+batched_step = jax.jit(jax.vmap(step, in_axes=(0, 0)))
+batched_legal_mask = jax.jit(jax.vmap(legal_action_mask))
+batched_obs = jax.jit(jax.vmap(observation, in_axes=(0, 0)))
+
+__all__ = [
+    "batched_reset",
+    "batched_reset_from_order",
+    "batched_step",
+    "batched_legal_mask",
+    "batched_obs",
+]

src/lost_cities_jax/engine.py

@@ -0,0 +1,285 @@
+"""Pure JAX rules engine for one round of two-player Lost Cities."""
+
+from __future__ import annotations
+
+import jax
+import jax.numpy as jnp
+
+from lost_cities_jax.types import (
+    CARDS_PER_COLOR,
+    DISCARD,
+    DRAW_DECK,
+    HAND_SIZE,
+    INITIAL_DEAL,
+    LOC_DECK,
+    LOC_DISCARD,
+    LOC_P0_BOARD,
+    LOC_P0_HAND,
+    MAX_PILE_SIZE,
+    MAX_STEPS,
+    N_ACTIONS,
+    N_CARDS,
+    N_COLORS,
+    NO_CARD,
+    PLAY,
+    UNKNOWN_CARD,
+    State,
+)
+
+
+def reset(rng: jax.Array) -> State:
+    """Return a freshly shuffled initial state."""
+
+    deck_order = jax.random.permutation(rng, jnp.arange(N_CARDS, dtype=jnp.int32), axis=0).astype(
+        jnp.int8
+    )
+    return reset_from_order(deck_order)
+
+
+def reset_from_order(deck_order: jax.Array) -> State:
+    """Return an initial state using an explicit 60-card deck permutation.
+
+    The first eight cards are dealt to player 0 and the next eight to player 1.
+    The remaining cards are drawn in array order starting at ``draw_ptr == 16``.
+    """
+
+    deck_order = jnp.asarray(deck_order, dtype=jnp.int8)
+    card_loc = jnp.full((N_CARDS,), LOC_DECK, dtype=jnp.int8)
+    p0_hand = deck_order[:HAND_SIZE].astype(jnp.int32)
+    p1_hand = deck_order[HAND_SIZE:INITIAL_DEAL].astype(jnp.int32)
+    card_loc = card_loc.at[p0_hand].set(jnp.int8(LOC_P0_HAND))
+    card_loc = card_loc.at[p1_hand].set(jnp.int8(LOC_P0_HAND + 1))
+
+    return State(
+        deck_order=deck_order,
+        draw_ptr=jnp.asarray(INITIAL_DEAL, dtype=jnp.int32),
+        card_loc=card_loc,
+        hand_public=jnp.zeros((N_CARDS,), dtype=jnp.bool_),
+        col_top=jnp.zeros((2, N_COLORS), dtype=jnp.int8),
+        col_hs=jnp.zeros((2, N_COLORS), dtype=jnp.int8),
+        col_len=jnp.zeros((2, N_COLORS), dtype=jnp.int8),
+        pile=jnp.full((N_COLORS, MAX_PILE_SIZE), NO_CARD, dtype=jnp.int8),
+        pile_len=jnp.zeros((N_COLORS,), dtype=jnp.int8),
+        to_move=jnp.asarray(0, dtype=jnp.int8),
+        just_discarded=jnp.asarray(NO_CARD, dtype=jnp.int8),
+        step_count=jnp.asarray(0, dtype=jnp.int32),
+        done=jnp.asarray(False),
+    )
+
+
+def decode_action(action: jax.Array) -> tuple[jax.Array, jax.Array, jax.Array]:
+    action = jnp.asarray(action, dtype=jnp.int32)
+    hand_slot = action // (2 * 6)
+    rem = action % (2 * 6)
+    place_type = rem // 6
+    draw_source = rem % 6
+    return hand_slot, place_type, draw_source
+
+
+def current_hand_sorted(state: State, player: jax.Array | int | None = None) -> jax.Array:
+    """Return the selected player's hand as sorted card ids, padded with 60."""
+
+    if player is None:
+        player = state.to_move
+    player = jnp.asarray(player, dtype=jnp.int32)
+    ids = jnp.arange(N_CARDS, dtype=jnp.int32)
+    hand_loc = LOC_P0_HAND + player
+    sortable = jnp.where(state.card_loc.astype(jnp.int32) == hand_loc, ids, UNKNOWN_CARD)
+    return jnp.sort(sortable)[:HAND_SIZE]
+
+
+def legal_action_mask(state: State) -> jax.Array:
+    """Return the 96-bit legal action mask for ``state.to_move``.
+
+    Illegal actions are masked out before placement and draw are considered as a
+    single atomic action. In particular, discarding to a color and then drawing
+    from that same discard pile is illegal because the top card would be the
+    just-discarded card.
+    """
+
+    player = state.to_move.astype(jnp.int32)
+    cards = current_hand_sorted(state, player).astype(jnp.int32)
+    valid_card = cards < N_CARDS
+    color = cards // CARDS_PER_COLOR
+    safe_color = jnp.clip(color, 0, N_COLORS - 1)
+    slot = cards % CARDS_PER_COLOR
+    is_handshake = slot < 3
+    rank = jnp.where(is_handshake, 0, slot - 1)
+
+    top_for_card = state.col_top[player, safe_color].astype(jnp.int32)
+    play_legal = valid_card & jnp.where(is_handshake, top_for_card == 0, rank > top_for_card)
+    discard_legal = valid_card
+    place_legal = jnp.stack([play_legal, discard_legal], axis=1)
+
+    src_colors = jnp.arange(N_COLORS, dtype=jnp.int32)
+    pre_len = state.pile_len.astype(jnp.int32)
+    pre_top_idx = jnp.maximum(pre_len - 1, 0)
+    pre_top = jnp.where(
+        pre_len > 0,
+        state.pile[src_colors, pre_top_idx].astype(jnp.int32),
+        NO_CARD,
+    )
+
+    place_types = jnp.arange(2, dtype=jnp.int32)
+    is_discard_place = place_types == DISCARD
+    same_discard_pile = is_discard_place[None, :, None] & (
+        safe_color[:, None, None] == src_colors[None, None, :]
+    )
+    after_len = pre_len[None, None, :] + same_discard_pile.astype(jnp.int32)
+    after_top = jnp.where(same_discard_pile, cards[:, None, None], pre_top[None, None, :])
+    just_discarded = jnp.where(
+        is_discard_place[None, :, None],
+        cards[:, None, None],
+        state.just_discarded.astype(jnp.int32),
+    )
+    pile_draw_legal = (after_len > 0) & (after_top != just_discarded)
+    deck_draw_legal = jnp.ones((HAND_SIZE, 2, 1), dtype=jnp.bool_)
+    draw_legal = jnp.concatenate([deck_draw_legal, pile_draw_legal], axis=2)
+
+    mask = (place_legal[:, :, None] & draw_legal).reshape((N_ACTIONS,))
+    return mask & ~state.done
+
+
+def step(state: State, action: jax.Array) -> tuple[State, jax.Array, jax.Array]:
+    """Apply one atomic place+draw action.
+
+    Illegal actions are defined as a no-op with zero reward. Done states are
+    also no-op states, which keeps vmapped batches stable when games finish at
+    different plies. Training code is expected to prevent illegal actions by
+    sampling from ``legal_action_mask``.
+    """
+
+    action = jnp.asarray(action, dtype=jnp.int32)
+    in_range = (action >= 0) & (action < N_ACTIONS)
+    safe_action = jnp.clip(action, 0, N_ACTIONS - 1)
+    mask = legal_action_mask(state)
+    is_legal = in_range & mask[safe_action]
+
+    proposed = _apply_legal_step(state, safe_action)
+    next_state = jax.tree_util.tree_map(
+        lambda new, old: jnp.where(is_legal, new, old), proposed, state
+    )
+    terminal_reward = board_score(proposed)
+    reward = jnp.where(
+        is_legal & proposed.done, terminal_reward, jnp.zeros((2,), dtype=jnp.float32)
+    )
+    return next_state, reward, next_state.done
+
+
+def _apply_legal_step(state: State, action: jax.Array) -> State:
+    hand_slot, place_type, draw_source = decode_action(action)
+    player = state.to_move.astype(jnp.int32)
+    next_player = (1 - player).astype(jnp.int8)
+    hand_loc = (LOC_P0_HAND + player).astype(jnp.int8)
+    board_loc = (LOC_P0_BOARD + player).astype(jnp.int8)
+
+    hand = current_hand_sorted(state, player)
+    raw_card = hand[hand_slot].astype(jnp.int32)
+    card = jnp.clip(raw_card, 0, N_CARDS - 1)
+    color = card // CARDS_PER_COLOR
+    slot = card % CARDS_PER_COLOR
+    is_handshake = slot < 3
+    rank = (slot - 1).astype(jnp.int8)
+    is_play = place_type == PLAY
+    is_discard = place_type == DISCARD
+
+    placed_loc = jnp.where(is_play, board_loc, jnp.int8(LOC_DISCARD)).astype(jnp.int8)
+    card_loc = state.card_loc.at[card].set(placed_loc)
+    hand_public = state.hand_public.at[card].set(False)
+
+    play_delta = is_play.astype(jnp.int8)
+    col_len = state.col_len.at[player, color].add(play_delta)
+    col_hs = state.col_hs.at[player, color].add((is_play & is_handshake).astype(jnp.int8))
+    old_top = state.col_top[player, color]
+    new_top = jnp.where(is_play & ~is_handshake, rank, old_top).astype(jnp.int8)
+    col_top = state.col_top.at[player, color].set(new_top)
+
+    pile_insert_idx = state.pile_len[color].astype(jnp.int32)
+    pile_old_value = state.pile[color, pile_insert_idx]
+    pile_new_value = jnp.where(is_discard, card.astype(jnp.int8), pile_old_value).astype(jnp.int8)
+    pile = state.pile.at[color, pile_insert_idx].set(pile_new_value)
+    pile_len = state.pile_len.at[color].add(is_discard.astype(jnp.int8))
+
+    is_deck_draw = draw_source == DRAW_DECK
+    draw_src = jnp.clip(draw_source - 1, 0, N_COLORS - 1)
+    safe_ptr = jnp.minimum(state.draw_ptr, N_CARDS - 1)
+    deck_card = state.deck_order[safe_ptr].astype(jnp.int32)
+    pile_draw_len = pile_len[draw_src].astype(jnp.int32)
+    pile_top_idx = jnp.maximum(pile_draw_len - 1, 0)
+    pile_card = jnp.clip(pile[draw_src, pile_top_idx].astype(jnp.int32), 0, N_CARDS - 1)
+    drawn_card = jnp.where(is_deck_draw, deck_card, pile_card).astype(jnp.int32)
+
+    card_loc = card_loc.at[drawn_card].set(hand_loc)
+    hand_public = hand_public.at[drawn_card].set(~is_deck_draw)
+
+    drawn_pile_value = pile[draw_src, pile_top_idx]
+    pile = pile.at[draw_src, pile_top_idx].set(
+        jnp.where(is_deck_draw, drawn_pile_value, jnp.int8(NO_CARD)).astype(jnp.int8)
+    )
+    pile_len = pile_len.at[draw_src].add(jnp.where(is_deck_draw, 0, -1).astype(jnp.int8))
+
+    draw_ptr = state.draw_ptr + jnp.where(is_deck_draw, 1, 0).astype(jnp.int32)
+    step_count = state.step_count + 1
+    natural_done = is_deck_draw & (draw_ptr >= N_CARDS)
+    forced_done = step_count >= MAX_STEPS
+    done = natural_done | forced_done
+
+    return State(
+        deck_order=state.deck_order,
+        draw_ptr=draw_ptr,
+        card_loc=card_loc,
+        hand_public=hand_public,
+        col_top=col_top,
+        col_hs=col_hs,
+        col_len=col_len,
+        pile=pile,
+        pile_len=pile_len,
+        to_move=next_player,
+        just_discarded=jnp.asarray(NO_CARD, dtype=jnp.int8),
+        step_count=step_count,
+        done=done,
+    )
+
+
+def board_score(state: State) -> jax.Array:
+    """Score the currently played boards for both players."""
+
+    card_ids = jnp.arange(N_CARDS, dtype=jnp.int32)
+    colors = card_ids // CARDS_PER_COLOR
+    slots = card_ids % CARDS_PER_COLOR
+    ranks = jnp.where(slots >= 3, slots - 1, 0)
+
+    player_scores = []
+    for player in range(2):
+        board_loc = LOC_P0_BOARD + player
+        on_board = state.card_loc.astype(jnp.int32) == board_loc
+        rank_sums = []
+        for color in range(N_COLORS):
+            rank_sums.append(jnp.sum(jnp.where(on_board & (colors == color), ranks, 0)))
+        rank_sum = jnp.stack(rank_sums).astype(jnp.int32)
+        length = state.col_len[player].astype(jnp.int32)
+        handshakes = state.col_hs[player].astype(jnp.int32)
+        base = (rank_sum - 20) * (1 + handshakes)
+        bonus = jnp.where(length >= 8, 20, 0)
+        color_score = jnp.where(length > 0, base + bonus, 0)
+        player_scores.append(jnp.sum(color_score))
+
+    return jnp.stack(player_scores).astype(jnp.float32)
+
+
+def score(state: State) -> jax.Array:
+    """Final round score. This intentionally matches ``board_score``."""
+
+    return board_score(state)
+
+
+__all__ = [
+    "current_hand_sorted",
+    "decode_action",
+    "legal_action_mask",
+    "reset",
+    "reset_from_order",
+    "step",
+    "score",
+    "board_score",
+]

src/lost_cities_jax/obs.py

@@ -0,0 +1,90 @@
+"""Observation construction for the JAX Lost Cities engine."""
+
+from __future__ import annotations
+
+import jax
+import jax.numpy as jnp
+
+from lost_cities_jax.engine import board_score
+from lost_cities_jax.types import (
+    CARDS_PER_COLOR,
+    DECK_DRAWS,
+    HAND_SIZE,
+    LOC_DECK,
+    LOC_DISCARD,
+    LOC_P0_BOARD,
+    LOC_P0_HAND,
+    MAX_ABS_SCORE,
+    MAX_STEPS,
+    N_CARDS,
+    N_COLORS,
+    NO_CARD,
+    OBS_DIM,
+    State,
+)
+
+
+def observation(state: State, player: jax.Array) -> jax.Array:
+    """Return a masked player-view observation as ``float32[OBS_DIM]``."""
+
+    player = jnp.asarray(player, dtype=jnp.int32)
+    opponent = 1 - player
+    loc = state.card_loc.astype(jnp.int32)
+    ids = jnp.arange(N_CARDS, dtype=jnp.int32)
+    colors = ids // CARDS_PER_COLOR
+
+    my_hand_loc = LOC_P0_HAND + player
+    opp_hand_loc = LOC_P0_HAND + opponent
+    my_board_loc = LOC_P0_BOARD + player
+    opp_board_loc = LOC_P0_BOARD + opponent
+
+    pile_len = state.pile_len.astype(jnp.int32)
+    top_idx = jnp.maximum(pile_len - 1, 0)
+    pile_tops = jnp.where(
+        pile_len > 0,
+        state.pile[jnp.arange(N_COLORS), top_idx].astype(jnp.int32),
+        NO_CARD,
+    )
+    is_discard = loc == LOC_DISCARD
+    is_discard_top = is_discard & (pile_tops[colors] == ids)
+    is_discard_not_top = is_discard & ~is_discard_top
+    is_opp_hand = loc == opp_hand_loc
+    is_opp_public = is_opp_hand & state.hand_public
+
+    channels = jnp.stack(
+        [
+            loc == my_hand_loc,
+            loc == my_board_loc,
+            loc == opp_board_loc,
+            is_discard_top,
+            is_discard_not_top,
+            is_opp_public,
+            (loc == LOC_DECK) | (is_opp_hand & ~state.hand_public),
+        ],
+        axis=1,
+    ).astype(jnp.float32)
+
+    scores = board_score(state)
+    score_diff = ((scores[player] - scores[opponent]) / MAX_ABS_SCORE).reshape((1,))
+    scalars = jnp.concatenate(
+        [
+            ((N_CARDS - state.draw_ptr).astype(jnp.float32) / DECK_DRAWS).reshape((1,)),
+            (jnp.sum(is_opp_hand & ~state.hand_public).astype(jnp.float32) / HAND_SIZE).reshape(
+                (1,)
+            ),
+            (state.step_count.astype(jnp.float32) / MAX_STEPS).reshape((1,)),
+            state.col_top[player].astype(jnp.float32) / 10.0,
+            state.col_top[opponent].astype(jnp.float32) / 10.0,
+            state.col_hs[player].astype(jnp.float32) / 3.0,
+            state.col_hs[opponent].astype(jnp.float32) / 3.0,
+            state.col_len[player].astype(jnp.float32) / CARDS_PER_COLOR,
+            state.col_len[opponent].astype(jnp.float32) / CARDS_PER_COLOR,
+            score_diff.astype(jnp.float32),
+        ],
+        axis=0,
+    )
+    obs = jnp.concatenate([channels.reshape((-1,)), scalars.astype(jnp.float32)], axis=0)
+    return obs.reshape((OBS_DIM,))
+
+
+__all__ = ["observation"]

src/lost_cities_jax/types.py

@@ -0,0 +1,59 @@
+"""Shared constants and state types for the JAX Lost Cities engine."""
+
+from __future__ import annotations
+
+from typing import NamedTuple
+
+import jax
+
+N_PLAYERS = 2
+N_COLORS = 5
+CARDS_PER_COLOR = 12
+N_CARDS = N_COLORS * CARDS_PER_COLOR
+HAND_SIZE = 8
+INITIAL_DEAL = N_PLAYERS * HAND_SIZE
+DECK_DRAWS = N_CARDS - INITIAL_DEAL
+MAX_PILE_SIZE = CARDS_PER_COLOR + 1
+
+N_PLACE_TYPES = 2
+N_DRAW_SOURCES = 6
+N_ACTIONS = HAND_SIZE * N_PLACE_TYPES * N_DRAW_SOURCES
+
+MAX_STEPS = 400
+
+CARD_CHANNELS = 7
+CARD_OBS_DIM = N_CARDS * CARD_CHANNELS
+SCALAR_OBS_DIM = 34
+OBS_DIM = CARD_OBS_DIM + SCALAR_OBS_DIM
+
+LOC_DECK = 0
+LOC_P0_HAND = 1
+LOC_P1_HAND = 2
+LOC_P0_BOARD = 3
+LOC_P1_BOARD = 4
+LOC_DISCARD = 5
+
+PLAY = 0
+DISCARD = 1
+DRAW_DECK = 0
+
+UNKNOWN_CARD = N_CARDS
+NO_CARD = -1
+
+MAX_ABS_SCORE = 780.0
+
+
+class State(NamedTuple):
+    deck_order: jax.Array
+    draw_ptr: jax.Array
+    card_loc: jax.Array
+    hand_public: jax.Array
+    col_top: jax.Array
+    col_hs: jax.Array
+    col_len: jax.Array
+    pile: jax.Array
+    pile_len: jax.Array
+    to_move: jax.Array
+    just_discarded: jax.Array
+    step_count: jax.Array
+    done: jax.Array

uv.lock

@@ -1,6 +1,20 @@
 version = 1
 revision = 3
 requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d0/4f/d79676ab82f2e42fc3611618139f13a9c4c31d0cff4b486982047679a802/absl_py-2.5.0.tar.gz", hash = "sha256:0c996f25c0490700fadabe6351630f6111534fa0ae252cc6d2014ea3b141135f", size = 118119, upload-time = "2026-07-03T10:57:48.157Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/58/0a/a10b45aab35b175aded078a462dc8d0c698f5b13946e7cb0869097b78bb6/absl_py-2.5.0-py3-none-any.whl", hash = "sha256:0f17b89f2a4eaaedc4f28c622998aa690564b3012a396a4ffad0821007fe03ba", size = 137410, upload-time = "2026-07-03T10:57:46.735Z" },
+]
 
 [[package]]
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@@ -118,6 +132,23 @@ wheels = [
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 dependencies = [
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