diff --git a/pgx/experimental/torch/go.py b/pgx/experimental/torch/go.py
new file mode 100644
index 000000000..4615bf6c4
--- /dev/null
+++ b/pgx/experimental/torch/go.py
@@ -0,0 +1,328 @@
+from functools import partial
+from typing import NamedTuple, Optional
+
+import torch
+from torch import Tensor
+import torch.nn.functional as F
+
+from lax import select, cond, scan, switch, fori_loop, while_loop
+from utils import At
+
+
+class GameState(NamedTuple):
+    step_count: Tensor = torch.tensor(0, dtype=torch.int32)
+    chain_id_board: Tensor = torch.zeros(19 * 19, dtype=torch.int32)
+    board_history: Tensor = torch.full((8, 19 * 19), 2, dtype=torch.int32)
+    num_captured_stones: Tensor = torch.zeros(2, dtype=torch.int32)
+    consecutive_pass_count: Tensor = torch.tensor(0, dtype=torch.int32)
+    ko: Tensor = torch.tensor(-1, dtype=torch.int32)
+    is_psk: Tensor = torch.tensor(False, dtype=torch.bool)
+
+    @property
+    def color(self) -> Tensor:
+        return self.step_count % 2
+
+    @property
+    def size(self) -> Tensor:
+        return torch.sqrt(torch.tensor(self.chain_id_board.shape[-1], dtype=torch.float32)).to(torch.int32)
+
+
+class Game:
+    def __init__(self, size: int = 19, komi: float = 7.5, history_length: int = 8, max_termination_steps: Optional[int] = None):
+        self.size = size
+        self.komi = komi
+        self.history_length = history_length
+        self.max_termination_steps = size * size * 2 if max_termination_steps is None else max_termination_steps
+
+    def init(self) -> GameState:
+        return GameState(
+            chain_id_board=torch.zeros(self.size**2, dtype=torch.int32),
+            board_history=torch.full((self.history_length, self.size**2), 2, dtype=torch.int32),
+        )
+
+    def step(self, state: GameState, action: Tensor) -> GameState:
+        state = state._replace(ko=torch.tensor(-1, dtype=torch.int32))
+        is_pass = torch.tensor(action == self.size * self.size)
+        pass_count = select(is_pass, state.consecutive_pass_count + 1, torch.tensor(0, dtype=torch.int32))
+        state = state._replace(consecutive_pass_count=pass_count)
+        if not is_pass:
+            state = _apply_action(state, action, self.size)
+        state = state._replace(step_count=state.step_count + 1)
+        board_history = torch.roll(state.board_history, self.size**2)
+        board_history = At(board_history)[0].set(torch.clamp(state.chain_id_board, -1, 1).to(torch.int32))
+        state = state._replace(board_history=board_history)
+        state = state._replace(is_psk=_check_PSK(state))
+        return state
+
+    def observe(self, state: GameState, color: Optional[Tensor] = None) -> Tensor:
+        if color is None:
+            color = state.color
+
+        my_color_sign = torch.tensor([1, -1], dtype=torch.int32)[color]
+
+        def _make(i):
+            c = torch.tensor([1, -1], dtype=torch.int32)[i % 2] * my_color_sign
+            return state.board_history[i // 2] == c
+
+        log = torch.vmap(_make)(torch.arange(self.history_length * 2))
+        color = torch.full_like(log[0], color)
+
+        return torch.stack([log, color]).transpose(0, 1).reshape((self.size, self.size, -1))
+
+    def legal_action_mask(self, state: GameState) -> Tensor:
+        is_empty = state.chain_id_board == 0
+
+        my_color = _my_color(state)
+        opp_color = _opponent_color(state)
+        num_pseudo, idx_sum, idx_squared_sum = _count(state, self.size)
+
+        chain_ix = torch.abs(state.chain_id_board) - 1
+        in_atari = (idx_sum[chain_ix] ** 2) == idx_squared_sum[chain_ix] * num_pseudo[chain_ix]
+        has_liberty = (state.chain_id_board * my_color > 0) & ~in_atari
+        kills_opp = (state.chain_id_board * opp_color > 0) & in_atari
+
+        def is_neighbor_ok(xy):
+            neighbors = _neighbour(xy, self.size)
+            on_board = neighbors != -1
+            _has_empty = is_empty[neighbors]
+            _has_liberty = has_liberty[neighbors]
+            _kills_opp = kills_opp[neighbors]
+            return (on_board & _has_empty).any() | (on_board & _kills_opp).any() | (on_board & _has_liberty).any()
+
+        neighbor_ok = torch.vmap(is_neighbor_ok)(torch.arange(self.size**2))
+        legal_action_mask = is_empty & neighbor_ok
+
+        legal_action_mask = cond(
+            (state.ko == -1),
+            lambda: legal_action_mask,
+            lambda: At(legal_action_mask)[state.ko].set(False),
+        )
+        return torch.cat([legal_action_mask, torch.tensor([True])])
+
+    def is_terminal(self, state: GameState) -> Tensor:
+        two_consecutive_pass = state.consecutive_pass_count >= 2
+        timeover = self.max_termination_steps <= state.step_count
+        return two_consecutive_pass | state.is_psk | timeover
+
+    def rewards(self, state: GameState) -> Tensor:
+        score = _count_point(state, self.size)
+        rewards = select(
+            score[0] - self.komi > score[1],
+            torch.tensor([1, -1], dtype=torch.float32),
+            torch.tensor([-1, 1], dtype=torch.float32),
+        )
+        to_play = state.color
+        rewards = select(state.is_psk, At(torch.tensor([-1, -1], dtype=torch.float32))[to_play].set(1.0), rewards)
+        rewards = select(self.is_terminal(state), rewards, torch.zeros(2, dtype=torch.float32))
+        return rewards
+
+
+def _apply_action(state: GameState, action, size) -> GameState:
+    xy = action
+    num_captured_stones_before = state.num_captured_stones[state.color]
+
+    ko_may_occur = _ko_may_occur(state, xy, size)
+
+    adj_xy = _neighbour(xy, size)
+    oppo_color = _opponent_color(state)
+    chain_id = state.chain_id_board[adj_xy]
+    num_pseudo, idx_sum, idx_squared_sum = _count(state, size)
+    chain_ix = torch.abs(chain_id) - 1
+    is_atari = (idx_sum[chain_ix] ** 2) == idx_squared_sum[chain_ix] * num_pseudo[chain_ix]
+    z = idx_sum[chain_ix]
+    z = torch.where(z == 0, z + 1, z)
+    single_liberty = (idx_squared_sum[chain_ix] // z) - 1
+    is_killed = (adj_xy != -1) & (chain_id * oppo_color > 0) & is_atari & (single_liberty == xy)
+    state = fori_loop(
+        0,
+        4,
+        lambda i, s: _remove_stones(is_killed[i], s, chain_id[i], adj_xy[i], ko_may_occur),
+        state,
+    )
+    state = _set_stone(state, xy)
+
+    state = fori_loop(0, 4, lambda i, s: _merge_around_xy(i, s, xy, size), state)
+
+    ko = select(
+        state.num_captured_stones[state.color] - num_captured_stones_before == 1,
+        state.ko,
+        torch.tensor(-1, dtype=torch.int32),
+    )
+
+    return state._replace(ko=ko)
+
+
+def _merge_around_xy(i, state: GameState, xy, size):
+    my_color = _my_color(state)
+    adj_xy = _neighbour(xy, size)[i]
+    is_off = adj_xy == -1
+    is_my_chain = state.chain_id_board[adj_xy] * my_color > 0
+    chain_id_board = cond(
+        ((~is_off) & is_my_chain),
+        lambda: _merge_chain(state, xy, adj_xy),
+        lambda: state.chain_id_board,
+    )
+    return state._replace(chain_id_board=chain_id_board)
+
+
+def _set_stone(state: GameState, xy) -> GameState:
+    my_color = _my_color(state)
+    return state._replace(
+        chain_id_board=At(state.chain_id_board)[xy].set((xy + 1) * my_color),
+    )
+
+
+def _merge_chain(state: GameState, xy, adj_xy):
+    my_color = _my_color(state)
+    new_id = torch.abs(state.chain_id_board[xy])
+    adj_chain_id = torch.abs(state.chain_id_board[adj_xy])
+    small_id = torch.minimum(new_id, adj_chain_id) * my_color
+    large_id = torch.maximum(new_id, adj_chain_id) * my_color
+
+    chain_id_board = torch.where(
+        state.chain_id_board == large_id,
+        small_id,
+        state.chain_id_board,
+    )
+
+    return chain_id_board
+
+
+def _remove_stones(killed, state: GameState, rm_chain_id, rm_stone_xy, ko_may_occur) -> GameState:
+    surrounded_stones = state.chain_id_board == rm_chain_id
+    num_captured_stones = torch.count_nonzero(surrounded_stones)
+    chain_id_board = torch.where(surrounded_stones, torch.tensor(0, dtype=torch.int32), state.chain_id_board)
+    ko = cond(
+        ko_may_occur & (num_captured_stones == 1),
+        lambda: torch.tensor(rm_stone_xy, dtype=torch.int32),
+        lambda: state.ko,
+    )
+    return state._replace(
+        chain_id_board=select(killed, chain_id_board, state.chain_id_board),
+        num_captured_stones=select(killed, At(state.num_captured_stones)[state.color].add(num_captured_stones), state.num_captured_stones),
+        ko=select(killed, ko, state.ko),
+    )
+
+
+def _count(state: GameState, size):
+    ZERO = torch.tensor(0, dtype=torch.int32)
+    chain_id_board = torch.abs(state.chain_id_board)
+    is_empty = chain_id_board == 0
+    idx_sum = torch.where(is_empty, torch.arange(1, size**2 + 1, dtype=torch.int32), ZERO)
+    idx_squared_sum = torch.where(is_empty, torch.arange(1, size**2 + 1, dtype=torch.int32) ** 2, ZERO)
+
+    def _count_neighbor(xy):
+        neighbors = _neighbour(xy, size)
+        on_board = neighbors != -1
+        return (
+            torch.where(on_board, is_empty[neighbors], ZERO).sum(),
+            torch.where(on_board, idx_sum[neighbors], ZERO).sum(),
+            torch.where(on_board, idx_squared_sum[neighbors], ZERO).sum(),
+        )
+
+    idx = torch.arange(size**2, dtype=torch.int32)
+    num_pseudo, idx_sum, idx_squared_sum = torch.vmap(_count_neighbor)(idx)
+
+    def _num_pseudo(x):
+        return torch.where(chain_id_board == (x + 1), num_pseudo, ZERO).sum()
+
+    def _idx_sum(x):
+        return torch.where(chain_id_board == (x + 1), idx_sum, ZERO).sum()
+
+    def _idx_squared_sum(x):
+        return torch.where(chain_id_board == (x + 1), idx_squared_sum, ZERO).sum()
+
+    return torch.vmap(_num_pseudo)(idx), torch.vmap(_idx_sum)(idx), torch.vmap(_idx_squared_sum)(idx)
+
+
+def _my_color(state: GameState):
+    return torch.tensor([1, -1], dtype=torch.int32)[state.color]
+
+
+def _opponent_color(state: GameState):
+    return torch.tensor([-1, 1], dtype=torch.int32)[state.color]
+
+
+def _ko_may_occur(state: GameState, xy: int, size: int) -> Tensor:
+    x = xy // size
+    y = xy % size
+    oob = torch.tensor([x - 1 < 0, x + 1 >= size, y - 1 < 0, y + 1 >= size], dtype=torch.bool)
+    oppo_color = _opponent_color(state)
+    is_occupied_by_opp = state.chain_id_board[_neighbour(xy, size)] * oppo_color > 0
+    return (oob | is_occupied_by_opp).all()
+
+
+def _neighbour(xy, size):
+    dx = torch.tensor([-1, +1, 0, 0], dtype=torch.int32)
+    dy = torch.tensor([0, 0, -1, +1], dtype=torch.int32)
+    xs = xy // size + dx
+    ys = xy % size + dy
+    on_board = (0 <= xs) & (xs < size) & (0 <= ys) & (ys < size)
+    return torch.where(on_board, xs * size + ys, -1)
+
+
+def _neighbours(size):
+    return torch.vmap(partial(_neighbour, size=size))(torch.arange(size**2, dtype=torch.int32))
+
+
+def _check_PSK(state: GameState):
+    not_passed = state.consecutive_pass_count == 0
+    is_psk = not_passed & (torch.abs(state.board_history[0] - state.board_history[1:]).sum(axis=1) == 0).any()
+    return is_psk
+
+
+def _count_point(state: GameState, size):
+    return torch.tensor(
+        [
+            _count_ji(state, 1, size) + torch.count_nonzero(state.chain_id_board > 0),
+            _count_ji(state, -1, size) + torch.count_nonzero(state.chain_id_board < 0),
+        ],
+        dtype=torch.float32,
+    )
+
+
+def _count_ji(state: GameState, color: int, size: int):
+    board = torch.zeros_like(state.chain_id_board)
+    board = torch.where(state.chain_id_board * color > 0, 1, board)
+    board = torch.where(state.chain_id_board * color < 0, -1, board)
+
+    neighbours = _neighbours(size)
+
+    def is_opp_neighbours(b):
+        return (b == 0) & ((b[neighbours.flatten()] == -1).reshape(size**2, 4) & (neighbours != -1)).any(axis=1)
+
+    def fill_opp(x):
+        b, _ = x
+        mask = is_opp_neighbours(b)
+        return torch.where(mask, -1, b), mask.any()
+
+    b, _ = while_loop(lambda x: x[1], fill_opp, (board, True))
+
+    return (b == 0).sum()
+
+
+if __name__ == '__main__':
+    actions = [45, 49, 17, 12, 22, 29, 31, 28, 46, 41, 33, 3, 8, 80, 13, 57, 48, 56, 7, 9, 75, 71, 58, 78, 23, 66, 77, 81, 79, 27, 47, 16, 72, 36, 21, 62, 14, 68, 54, 2, 5, 53, 76, 6, 64, 37, 15, 43, 70, 52, 40, 0, 74, 44, 10, 59, 60, 67, 63, 51, 25, 50, 18, 34, 11, 55, 6, 26, 39, 58, 32, 35, 16, 61, 73, 19, 30, 1, 81, 38, 42, 18, 81, 81]
+    expected = [-1, -1, -1, -1, 0, 6, 6, 6, 6, -1, 11, 11, -1, 6, 6, 6, 6, 6, -1, -1, 0, 6, 6, 6, 0, 6, -27, -1, -1, -1, 6, 6, 6, 6, -27, -27, -1, -1, -1, 6, 6, -27, 6, -27, -27, 6, 6, 6, 6, -27, -27, -27, -27, -27, 6, -27, -27, -27, -27, -27, 61, -27, -27, 6, 6, 0, -27, -27, -27, 0, 71, -27, 6, 6, 6, 6, 6, 6, -79, 71, -27]
+
+    game = Game(size=9)
+    init_fn = game.init
+    step_fn = game.step
+    legal_action_fn = game.legal_action_mask
+    terminal_fn = game.is_terminal
+
+    state = init_fn()
+    for i, action in enumerate(actions):
+        legal_action_mask = legal_action_fn(state)
+        assert legal_action_mask[action], f"action {action} is not legal"
+        assert not terminal_fn(state)
+        state = step_fn(state, action)
+
+    print("expected = ")
+    print(torch.tensor(expected).reshape(9, 9))
+    print("actual = ")
+    print(state.chain_id_board.reshape(9, 9))
+
+    assert torch.all(state.chain_id_board == torch.tensor(expected))
+    assert game.is_terminal(state)
+
diff --git a/pgx/experimental/torch/lax.py b/pgx/experimental/torch/lax.py
new file mode 100644
index 000000000..c65b9e52a
--- /dev/null
+++ b/pgx/experimental/torch/lax.py
@@ -0,0 +1,39 @@
+import torch
+
+
+def select(pred, on_true, on_false):
+    return torch.where(pred, on_true, on_false)
+
+
+def cond(pred, true_fun, false_fun, *operands):
+    return select(pred, true_fun(*operands), false_fun(*operands))
+
+
+def scan(f, init, xs, length=None):
+    if xs is None:
+        xs = [None] * length
+    carry = init
+    ys = []
+    for x in xs:
+        carry, y = f(carry, x)
+        ys.append(y)
+    return carry, torch.stack(ys)
+
+
+def switch(index, branches, *operands):
+    index = torch.clip(0, index, len(branches) - 1)
+    return branches[index](*operands)
+
+
+def fori_loop(lower, upper, body_fun, init_val):
+    val = init_val
+    for i in range(lower, upper):
+        val = body_fun(i, val)
+    return val
+
+
+def while_loop(cond_fun, body_fun, init_val):
+    val = init_val
+    while cond_fun(val):
+        val = body_fun(val)
+    return val
diff --git a/pgx/experimental/torch/utils.py b/pgx/experimental/torch/utils.py
new file mode 100644
index 000000000..a3d6ff85f
--- /dev/null
+++ b/pgx/experimental/torch/utils.py
@@ -0,0 +1,27 @@
+class At:
+    """A utility for JAX's x = x.at[i].set(val) API
+    >>> x = At(x)[i].set(val)
+    """
+    def __init__(self, array):
+        self.array = array
+
+    def __getitem__(self, index):
+        return _IndexUpdateRef(self.array, index)
+
+
+class _IndexUpdateRef:
+    def __init__(self, array, index):
+        self.array = array.clone()  # make immutable
+        self.index = index
+
+    def set(self, value):
+        self.array[self.index] = value
+        return self.array
+
+    def add(self, value):
+        self.array[self.index] += value
+        return self.array
+
+    # TODO: add all methods
+
+