tabular-game-state-grid-encoding

name	tabular-game-state-grid-encoding
description	Encode a 2D game board into a normalized multi-channel feature tensor with log-scaled resources, signed unit counts, and directional features for RL agents

Game State Grid Encoding

Overview

Game AI competitions often provide board state as nested objects (cells, fleets, shipyards). Converting this into a fixed-size tensor with per-cell feature channels enables CNN or MLP-based RL agents. Each channel encodes a different aspect: log-scaled resources, signed unit counts (+1 for friendly, -1 for enemy), direction values, and structure presence.

Quick Start

import numpy as np

N_FEATURES = 4  # kore, ships, direction, shipyard

def encode_board(board, player_id):
    size = board.configuration.size
    state = np.zeros((size, size, N_FEATURES), dtype=np.float32)
    for point, cell in board.cells.items():
        state[point.y, point.x, 0] = cell.kore
        if cell.fleet:
            sign = 1 if cell.fleet.player_id == player_id else -1
            state[point.y, point.x, 1] = sign * cell.fleet.ship_count
            state[point.y, point.x, 2] = cell.fleet.direction.value
        if cell.shipyard:
            state[point.y, point.x, 3] = 1 if cell.shipyard.player_id == player_id else -1
    state[:, :, 0] = np.clip(np.log2(state[:, :, 0] + 1) / 10.0, 0, 1)
    state[:, :, 1] = np.clip(state[:, :, 1] / 100.0, -1, 1)
    return state

Workflow

1. Initialize a zero tensor of shape (board_size, board_size, n_features)
2. Iterate over all cells; populate resource, unit, and structure channels
3. Sign-encode ownership: +1 for own units, -1 for enemy
4. Log-scale resources to compress dynamic range, then normalize to [0, 1]
5. Clip unit counts to [-1, 1] by dividing by expected max
6. Flatten or pass directly to a CNN policy network

Key Decisions

• Log scaling: resources often span 0 to 10,000+; log2 compresses this to ~13 bits
• Signed encoding: lets the network distinguish friend from foe without separate channels
• Toroidal wrapping: pad the tensor circularly if using CNNs to respect wrap-around adjacency
• Channel count: start minimal (4-6), add channels for cargo, turn number, spawn capacity as needed

References

• Reinforcement Learning baseline in Python