E-Rong/til-26-ae-agent

TIL-26-AE Bomberman Agent — MaskablePPO + Curriculum Learning

This repository contains the training pipeline for an RL agent competing in the TIL-26 Automated Exploration (AE) challenge — a competitive multi-agent Bomberman-like environment.

🎯 Challenge

Environment: 2–6 team competitive Bomberman on a procedurally generated 16×16 maze. Key challenges:

• Partial observability (directional viewcones, not full map)
• Sparse terminal rewards (±50 for base destroy/survival)
• Procedural generation (new maze every episode)
• Risk of camping near base without exploration signal

🏗️ Architecture

Three-Phase Training Pipeline

Phase	Description	Opponents	Key Technique
1	MaskablePPO baseline	Random valid actions	Invalid action masking
2	Adaptive exploration	Random + visit-count bonus	Annealing: α = 1 − tanh(k·deaths)
3	Curriculum self-play	Rule-based (static → smart)	Elo-style difficulty progression

Design Decisions (Literature-Backed)

1. MaskablePPO (sb3-contrib): Handles invalid actions by setting logits to -∞ before softmax. Proven superior to action penalties (Huang & Ontañón, 2020).
2. MAPPO-style hyperparameters: Value normalization, centralized value / decentralized policy, low sample reuse (Yu et al., NeurIPS 2022).
3. Adaptive exploration annealing: Directly from Pommerman SOTA (2024). As agent skill improves, exploration bonus decreases automatically, preventing camping.
4. Curriculum learning: 4 stages — static → simple → smart → mixed opponents. Advance at 55% win rate (or 500 episodes max).

Key Papers

• Pommerman multi-agent RL: arxiv:2407.00662 — 98.85% win rate recipe
• MAPPO best practices: arxiv:2103.01955 — NeurIPS 2022
• Invalid Action Masking: arxiv:2006.14171 — theoretically justified
• RND exploration (fallback): arxiv:1810.12894 — if Phase 2 still camps

🚀 Running Training

Prerequisites

# Download the environment (auto-bootstrapped in script)
python -c "from huggingface_hub import snapshot_download; snapshot_download('e-rong/til-26-ae', repo_type='space', local_dir='./til-26-ae-repo')"

Local Training

export TOTAL_TIMESTEPS="500_000:500_000:1_000_000"
export HUB_MODEL_ID="E-Rong/til-26-ae-agent"
export TRACKIO_PROJECT="til-26-ae"
python train_all_phases.py

HF Jobs (Recommended)

# Requires HF credits — run from a Space with the script uploaded
# Hardware: cpu-upgrade or a10g-large for GPU acceleration

📊 Monitoring

Trackio dashboard: E-Rong/til-26-ae-trackio

Logged metrics per phase:

• train/mean_episode_reward
• train/mean_episode_length
• train/mean_explore_bonus (Phase 2)
• train/curriculum_stage (Phase 3)

Alerts trigger on:

• Low reward (< -5) after 50k steps → suggests camping
• Curriculum stage advancement

📁 Repository Structure

train_all_phases.py   # Full 3-phase pipeline
requirements.txt      # Dependencies
bomberman_phase1_final.zip   # Saved after Phase 1
bomberman_phase2_final.zip   # Saved after Phase 2
bomberman_phase3_final.zip   # Saved after Phase 3

🧪 Evaluation

To evaluate a trained agent against random opponents:

from train_all_phases import BombermanSingleAgentEnv
from sb3_contrib import MaskablePPO
from til_environment.config import default_config

cfg = default_config()
env = BombermanSingleAgentEnv(cfg=cfg)
model = MaskablePPO.load("bomberman_phase3_final")

obs, _ = env.reset(seed=42)
for _ in range(200):
    action, _ = model.predict(obs, action_masks=env.action_masks())
    obs, reward, done, truncated, info = env.step(action)
    if done or truncated:
        break
env.close()

📜 License

MIT — based on the TIL-26 AE challenge environment.

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "E-Rong/til-26-ae-agent"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.