cv-smm-spatial-observation-encoding

name	cv-smm-spatial-observation-encoding
description	Encode game state as a Super Mini Map (SMM) with separate binary channels for players, ball, and ownership, bit-packed for efficient transfer in RL training

SMM Spatial Observation Encoding

Overview

For RL agents in spatial games (football, RTS), raw observation vectors (player positions as floats) lose spatial relationships. The Super Mini Map (SMM) renders game state onto a small 2D grid with separate binary channels: one for left team positions, one for right team, one for ball, one for active player. This gives CNN policies a spatial inductive bias while staying compact via bit-packing.

Quick Start

import numpy as np
from collections import deque

def render_smm(obs, width=96, height=72):
    smm = np.zeros((height, width, 4), dtype=np.uint8)
    def plot(positions, channel):
        for x, y in positions:
            px = int((x + 1) / 2 * (width - 1))
            py = int((y + 0.42) / 0.84 * (height - 1))
            px, py = np.clip(px, 0, width-1), np.clip(py, 0, height-1)
            smm[py, px, channel] = 255
    plot(obs["left_team"], 0)
    plot(obs["right_team"], 1)
    plot([obs["ball"][:2]], 2)
    plot([obs["left_team"][obs["active"]]], 3)
    return smm

# Frame stacking with bit-packing
frames = deque(maxlen=4)
obs = render_smm(raw_obs)
frames.extend([obs] * 4)
stacked = np.concatenate(list(frames), axis=-1)
packed = np.packbits(stacked, axis=-1)

Workflow

1. Define a small grid (72x96 or 48x64) matching the field's aspect ratio
2. Map normalized positions to pixel coordinates with boundary clipping
3. Render each entity type into a separate binary channel
4. Stack 4 consecutive frames for temporal context (16 channels total)
5. Bit-pack the binary arrays for 8x memory reduction during transfer
6. Feed to a CNN policy network (Conv2D → Dense → action logits)

Key Decisions

• Resolution: 72x96 balances spatial precision vs. computation; lower res for faster training
• Channels: 4 base channels (left team, right team, ball, active player); add ownership, direction as needed
• Frame stacking: 4 frames captures velocity without explicit velocity channels
• Bit-packing: reduces observation size 8x for distributed RL data transfer; unpack before inference
• vs. raw vectors: SMM gives CNN spatial inductive bias; raw vectors are better for MLP policies

References

• GFootball - train SEED RL agent
• Convolutional Deep-Q learner