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| |
| import os, sys, json, time, random |
|
|
| os.chdir('/content/qwenjev') |
| sys.path.insert(0, '/content/qwenjev') |
| sys.path.insert(0, '/content/qwenjev/train') |
|
|
| from data.collect_selfplay import make_env, meta_from |
| from data.mock_env import step_env |
|
|
| GAMES = [('bp35', 150)] |
| BASE_SEED = 42 |
|
|
| report = [] |
| t0 = time.time() |
| for idx, (game_id, max_actions) in enumerate(GAMES): |
| rng = random.Random(BASE_SEED + idx * 100) |
| env = make_env(game_id, None) |
| env.reset() |
| actions = 0 |
| resets = 0 |
| best_level = 0 |
| final_state = 'NOT_FINISHED' |
| started = time.time() |
| action_hist = {} |
| for _ in range(max_actions): |
| meta = meta_from(env) |
| final_state = meta['state'] |
| if final_state == 'WIN': |
| break |
| if final_state == 'GAME_OVER': |
| env.reset() |
| resets += 1 |
| continue |
| best_level = max(best_level, meta['levels_completed']) |
| avail = list(meta['available_actions']) or ['ACTION1'] |
| a = rng.choice([x for x in avail if x != 'RESET'] or ['ACTION1']) |
| action_hist[a] = action_hist.get(a, 0) + 1 |
| step_env(env, a) |
| actions += 1 |
| best_level = max(best_level, meta_from(env)['levels_completed']) |
| rec = { |
| 'game_id': game_id, |
| 'win': final_state == 'WIN', |
| 'final_state': final_state, |
| 'best_level': best_level, |
| 'resets': resets, |
| 'actions': actions, |
| 'seconds': round(time.time() - started, 1), |
| 'action_hist': action_hist, |
| } |
| report.append(rec) |
| print('RESULT', json.dumps(rec), flush=True) |
|
|
| out = { |
| 'meta': { |
| 'backend': 'random-uniform', 'seed_note_v2': 'budget paired with v4b-600 ar25 probe', |
| 'seed_note': 'rng = Random(42 + idx*100) per run, fixed for reproducibility', |
| 'games': [f'{g}@{m}' for g, m in GAMES], |
| 'max_actions': [m for _, m in GAMES], |
| 'seconds_total': round(time.time() - t0, 1), |
| 'timestamp': time.strftime('%F %T'), |
| }, |
| 'games': report, |
| 'summary': { |
| 'played': len(report), |
| 'wins': sum(1 for r in report if r['win']), |
| 'best_level_sum': sum(r['best_level'] for r in report), |
| 'resets_sum': sum(r['resets'] for r in report), |
| 'actions_sum': sum(r['actions'] for r in report), |
| }, |
| } |
| open('/content/eval_random_baseline.json', 'w').write(json.dumps(out, indent=2)) |
| print('SUMMARY', json.dumps(out['summary']), flush=True) |
|
|
