AUTORESEARCH: Full Karpathy-style GP+UCB meta-controller, clean base data, fixed all paths, ready to run
This commit is contained in:
Paul Huliganga
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# DonkeyCar RL Autoresearch - README
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# ===================================
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#
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# QUICK START (after simulator is running):
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#
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# cd /home/paulh/projects/donkeycar-rl-autoresearch/agent
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# python3 autoresearch_controller.py --trials 100
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#
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# The autoresearch will:
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# 1. Load all base sweep data (clean_sweep_results.jsonl)
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# 2. Fit a Gaussian Process surrogate model on reward-vs-params
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# 3. Use UCB (Upper Confidence Bound) to propose next best params
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# 4. Launch RL jobs automatically via the robust runner
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# 5. Record all results to outerloop-results/autoresearch_results.jsonl
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# 6. Repeat for --trials iterations, learning as it goes
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#
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# You can stop at any time with Ctrl+C.
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# Restart and it automatically picks up all prior results.
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#
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# LOGS:
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# outerloop-results/autoresearch_log.txt - human-readable log
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# outerloop-results/autoresearch_results.jsonl - all trial results (JSON)
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# outerloop-results/clean_sweep_results.jsonl - base sweep data
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#
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# TUNING:
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# --trials N : number of autoresearch trials (default 100)
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# --explore K : UCB kappa, higher = more exploration (default 2.0)
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#
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# HOW IT WORKS (Karpathy-style autoresearch):
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# - A Gaussian Process (GP) is fit on all existing (params, reward) pairs
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# - The GP models the unknown reward function over the parameter space
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# - UCB acquisition = GP mean + kappa * GP uncertainty
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# - The next trial uses the params that maximize UCB
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# - This intelligently balances exploiting known good regions vs
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# exploring uncertain regions - far smarter than any fixed grid
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#
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# PARAMETER SPACE EXPLORED (continuously, not just grid values):
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# n_steer: 3 to 9 (integer)
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# n_throttle: 2 to 5 (integer)
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# learning_rate: 0.00005 to 0.005 (float)
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"""
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=============================================================
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DonkeyCar RL Autoresearch Controller
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Karpathy-style meta-agent that:
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1. Loads base sweep data
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2. Builds a surrogate model (Gaussian Process) of reward landscape
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3. Uses Upper Confidence Bound (UCB) acquisition to propose next params
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4. Launches RL jobs via robust runner
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5. Records results and iterates autonomously
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=============================================================
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Usage:
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python3 autoresearch_controller.py [--trials N] [--explore K]
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All results are appended to:
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outerloop-results/autoresearch_results.jsonl
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outerloop-results/autoresearch_log.txt
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Stop at any time with Ctrl+C. Restart and it picks up from existing data.
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=============================================================
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"""
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import os
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import sys
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import json
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import time
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import subprocess
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import itertools
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import re
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import numpy as np
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from datetime import datetime
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# ---- Paths ----
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PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
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RUNNER_SCRIPT = os.path.join(PROJECT_DIR, 'donkeycar_sb3_runner.py')
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RESULTS_DIR = os.path.join(PROJECT_DIR, 'outerloop-results')
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BASE_DATA_FILE = os.path.join(RESULTS_DIR, 'clean_sweep_results.jsonl')
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AUTORESEARCH_RESULTS = os.path.join(RESULTS_DIR, 'autoresearch_results.jsonl')
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AUTORESEARCH_LOG = os.path.join(RESULTS_DIR, 'autoresearch_log.txt')
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os.makedirs(RESULTS_DIR, exist_ok=True)
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# ---- Parameter Space Definition ----
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# These define the bounds for the autoresearch to explore.
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# Autoresearch can propose any value within these continuous ranges.
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PARAM_SPACE = {
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'n_steer': {'type': 'int', 'min': 3, 'max': 9},
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'n_throttle': {'type': 'int', 'min': 2, 'max': 5},
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'learning_rate': {'type': 'float', 'min': 0.00005,'max': 0.005},
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}
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# Fixed params for all runs
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FIXED_PARAMS = {
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'timesteps': 2000,
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'eval_episodes': 3,
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}
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# How many candidate proposals to sample when searching for next best
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N_CANDIDATES = 500
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# UCB exploration constant (higher = more exploration)
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UCB_KAPPA = 2.0
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# Job timeout seconds
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JOB_TIMEOUT = 360
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# ---- Logging ----
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def log(msg):
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ts = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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line = f'[{ts}] {msg}'
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print(line, flush=True)
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with open(AUTORESEARCH_LOG, 'a') as f:
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f.write(line + '\n')
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# ---- Parameter Encoding (for surrogate model) ----
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PARAM_KEYS = list(PARAM_SPACE.keys())
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def encode_params(params):
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"""Encode a params dict into a normalized numpy vector [0,1] for the GP."""
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vec = []
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for k in PARAM_KEYS:
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spec = PARAM_SPACE[k]
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v = params[k]
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norm = (v - spec['min']) / (spec['max'] - spec['min'])
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vec.append(norm)
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return np.array(vec)
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def decode_params(vec):
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"""Decode a normalized numpy vector back to a params dict."""
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params = {}
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for i, k in enumerate(PARAM_KEYS):
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spec = PARAM_SPACE[k]
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v = vec[i] * (spec['max'] - spec['min']) + spec['min']
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if spec['type'] == 'int':
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v = int(round(v))
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v = max(spec['min'], min(spec['max'], v))
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else:
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v = float(v)
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v = max(spec['min'], min(spec['max'], v))
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params[k] = v
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return params
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def random_candidate():
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"""Sample a random candidate in the parameter space."""
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vec = np.random.uniform(0, 1, len(PARAM_KEYS))
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return vec
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# ---- Gaussian Process Surrogate Model (pure numpy, no sklearn needed) ----
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class TinyGP:
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"""
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Minimal Gaussian Process regressor (RBF kernel) for surrogate modelling.
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Predicts mean and std of reward for any parameter vector.
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"""
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def __init__(self, length_scale=0.3, noise=1e-3):
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self.ls = length_scale
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self.noise = noise
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self.X = None
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self.y = None
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self.K_inv = None
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def _rbf(self, X1, X2):
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"""RBF kernel matrix between X1 and X2."""
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diff = X1[:, np.newaxis, :] - X2[np.newaxis, :, :]
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sq = np.sum(diff**2, axis=-1)
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return np.exp(-sq / (2 * self.ls**2))
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def fit(self, X, y):
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self.X = np.array(X)
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self.y = np.array(y)
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n = len(y)
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K = self._rbf(self.X, self.X) + self.noise * np.eye(n)
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try:
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self.K_inv = np.linalg.inv(K)
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except np.linalg.LinAlgError:
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self.K_inv = np.linalg.pinv(K)
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self.alpha = self.K_inv @ self.y
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def predict(self, X_new):
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"""Returns (mean, std) arrays for each row in X_new."""
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X_new = np.atleast_2d(X_new)
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K_s = self._rbf(X_new, self.X)
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mean = K_s @ self.alpha
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K_ss = np.ones(len(X_new)) + self.noise
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var = K_ss - np.sum((K_s @ self.K_inv) * K_s, axis=1)
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var = np.maximum(var, 1e-9)
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return mean, np.sqrt(var)
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# ---- Load All Available Data (base sweep + autoresearch results) ----
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def load_all_results():
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"""Load all param-reward pairs from base sweep and any autoresearch runs."""
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results = []
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for fpath in [BASE_DATA_FILE, AUTORESEARCH_RESULTS]:
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if not os.path.exists(fpath):
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continue
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with open(fpath) as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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try:
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rec = json.loads(line)
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mr = rec.get('mean_reward')
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if mr is not None:
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results.append({'params': rec['params'], 'mean_reward': float(mr)})
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except Exception:
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pass
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return results
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# ---- UCB Acquisition: Propose Next Best Parameters ----
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def propose_next_params(results, n_candidates=N_CANDIDATES, kappa=UCB_KAPPA):
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"""
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Fit GP on existing results, then maximize UCB acquisition
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over random candidate samples to propose the next params to try.
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Returns: proposed params dict
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"""
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if len(results) < 2:
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log('[AutoResearch] Not enough data for GP yet, using random proposal.')
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return decode_params(random_candidate())
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X = np.array([encode_params(r['params']) for r in results])
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y = np.array([r['mean_reward'] for r in results])
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# Normalize y for numerical stability
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y_mean = y.mean()
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y_std = y.std() if y.std() > 0 else 1.0
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y_norm = (y - y_mean) / y_std
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gp = TinyGP(length_scale=0.3, noise=1e-3)
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gp.fit(X, y_norm)
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# Sample candidates
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candidates = np.random.uniform(0, 1, (n_candidates, len(PARAM_KEYS)))
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# Compute UCB acquisition
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mu, sigma = gp.predict(candidates)
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ucb = mu + kappa * sigma
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best_idx = np.argmax(ucb)
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best_vec = candidates[best_idx]
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proposed = decode_params(best_vec)
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# Log the GP's top predictions
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top5_idx = np.argsort(ucb)[-5:][::-1]
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log(f'[AutoResearch] GP UCB top-5 candidates:')
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for idx in top5_idx:
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p = decode_params(candidates[idx])
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log(f' UCB={ucb[idx]:.4f} mu={mu[idx]:.4f} sigma={sigma[idx]:.4f} params={p}')
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return proposed
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# ---- Kill Stale Jobs ----
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def kill_stale():
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subprocess.run(['pkill', '-9', '-f', 'donkeycar_sb3_runner.py'], check=False)
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time.sleep(2)
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# ---- Launch RL Job with Proposed Params ----
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def launch_job(params):
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"""Launch a single RL runner job and return (mean_reward, output, status)."""
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cmd = [
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'python3', RUNNER_SCRIPT,
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'--agent', 'dqn',
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'--env', 'donkey-generated-roads-v0',
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'--timesteps', str(params.get('timesteps', FIXED_PARAMS['timesteps'])),
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'--eval-episodes', str(params.get('eval_episodes', FIXED_PARAMS['eval_episodes'])),
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'--n-steer', str(params['n_steer']),
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'--n-throttle', str(params['n_throttle']),
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]
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log(f'[AutoResearch] Launching job: n_steer={params["n_steer"]} n_throttle={params["n_throttle"]} lr={params["learning_rate"]:.6f}')
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start = time.time()
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try:
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proc = subprocess.run(cmd, capture_output=True, text=True, timeout=JOB_TIMEOUT)
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elapsed = time.time() - start
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output = proc.stdout + '\n' + proc.stderr
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status = 'ok' if proc.returncode == 0 else 'error'
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log(f'[AutoResearch] Job finished in {elapsed:.1f}s, returncode={proc.returncode}')
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except subprocess.TimeoutExpired as e:
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elapsed = time.time() - start
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output = f'[TIMEOUT after {elapsed:.1f}s]'
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status = 'timeout'
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log(f'[AutoResearch] Job TIMED OUT after {elapsed:.1f}s')
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# Parse mean_reward from output
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mean_reward = None
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m = re.search(r'\[SB3 Runner\]\[TEST\] mean_reward=([\d.]+)', output)
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if m:
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mean_reward = float(m.group(1))
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log(f'[AutoResearch] mean_reward={mean_reward}')
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# Print full runner output for transparency
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print('--- Runner Output ---')
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print(output[-3000:]) # last 3000 chars
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print('--- End Runner Output ---')
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return mean_reward, output, status, elapsed
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# ---- Save Result ----
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def save_result(trial, params, mean_reward, status, elapsed):
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rec = {
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'trial': trial,
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'timestamp': datetime.now().isoformat(),
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'params': params,
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'mean_reward': mean_reward,
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'run_status': status,
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'elapsed_sec': elapsed,
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}
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with open(AUTORESEARCH_RESULTS, 'a') as f:
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f.write(json.dumps(rec) + '\n')
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# ---- Print Current Best ----
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def print_summary(results, trial):
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if not results:
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return
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best = max(results, key=lambda r: r['mean_reward'])
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log(f'[AutoResearch] === Trial {trial} Summary ===')
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log(f' Total runs in history: {len(results)}')
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log(f' Best so far: mean_reward={best["mean_reward"]:.4f} params={best["params"]}')
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# Top 5
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sorted_r = sorted(results, key=lambda r: r['mean_reward'], reverse=True)
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log(f' Top 5 results:')
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for r in sorted_r[:5]:
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log(f' mean_reward={r["mean_reward"]:.4f} params={r["params"]}')
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# ---- Main Autoresearch Loop ----
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def run_autoresearch(max_trials=100):
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log('=' * 60)
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log('[AutoResearch] Starting Karpathy-style autoresearch controller')
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log(f'[AutoResearch] Max trials: {max_trials}')
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log(f'[AutoResearch] Runner: {RUNNER_SCRIPT}')
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log(f'[AutoResearch] Results: {AUTORESEARCH_RESULTS}')
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log('=' * 60)
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# Load all existing data (base sweep + prior autoresearch runs)
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results = load_all_results()
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log(f'[AutoResearch] Loaded {len(results)} existing result(s) from base sweep + history.')
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print_summary(results, trial=0)
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for trial in range(1, max_trials + 1):
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log(f'\n[AutoResearch] ========== Trial {trial}/{max_trials} ==========')
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# 1. Propose next params using GP+UCB
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proposed = propose_next_params(results)
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full_params = {**proposed, **FIXED_PARAMS}
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log(f'[AutoResearch] Proposed params: {full_params}')
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# 2. Kill any stale jobs
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kill_stale()
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# 3. Launch job
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mean_reward, output, status, elapsed = launch_job(full_params)
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# 4. Save result
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save_result(trial, full_params, mean_reward, status, elapsed)
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# 5. If we got a valid reward, add to results for next GP fit
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if mean_reward is not None:
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results.append({'params': full_params, 'mean_reward': mean_reward})
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else:
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log(f'[AutoResearch] WARNING: No valid mean_reward from this trial.')
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# 6. Print running summary
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print_summary(results, trial)
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# 7. Brief pause between trials
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time.sleep(2)
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log('[AutoResearch] All trials complete!')
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print_summary(results, trial=max_trials)
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# ---- Entry Point ----
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Karpathy-style autoresearch controller for DonkeyCar RL.')
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parser.add_argument('--trials', type=int, default=100, help='Number of autoresearch trials to run (default: 100)')
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parser.add_argument('--explore', type=float, default=2.0, help='UCB exploration constant kappa (default: 2.0, higher=more explore)')
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args = parser.parse_args()
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UCB_KAPPA = args.explore
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run_autoresearch(max_trials=args.trials)
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@ -31,7 +31,7 @@ def build_param_combinations(grid):
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def run_sweep():
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results = []
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out_dir = '/home/paulh/.pi/agent/outerloop-results'
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out_dir = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/outerloop-results'
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os.makedirs(out_dir, exist_ok=True)
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log_file = os.path.join(out_dir, 'sweep_results.jsonl')
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{"config_id": 1, "params": {"n_steer": 3, "n_throttle": 2, "learning_rate": 0.001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 46.7312, "run": 1}
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{"config_id": 2, "params": {"n_steer": 3, "n_throttle": 2, "learning_rate": 0.0005, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 64.7249, "run": 2}
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{"config_id": 3, "params": {"n_steer": 3, "n_throttle": 2, "learning_rate": 0.0001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 36.2958, "run": 3}
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{"config_id": 4, "params": {"n_steer": 3, "n_throttle": 3, "learning_rate": 0.001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 33.6781, "run": 4}
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{"config_id": 5, "params": {"n_steer": 3, "n_throttle": 3, "learning_rate": 0.0005, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 59.7928, "run": 5}
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{"config_id": 6, "params": {"n_steer": 3, "n_throttle": 3, "learning_rate": 0.0001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 61.8774, "run": 6}
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{"config_id": 7, "params": {"n_steer": 5, "n_throttle": 2, "learning_rate": 0.001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 97.7536, "run": 7}
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{"config_id": 8, "params": {"n_steer": 5, "n_throttle": 2, "learning_rate": 0.0005, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 61.7233, "run": 8}
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{"config_id": 9, "params": {"n_steer": 5, "n_throttle": 2, "learning_rate": 0.0001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 53.6128, "run": 9}
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{"config_id": 10, "params": {"n_steer": 5, "n_throttle": 3, "learning_rate": 0.001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 50.171, "run": 10}
|
||||
{"config_id": 11, "params": {"n_steer": 5, "n_throttle": 3, "learning_rate": 0.0005, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 78.3455, "run": 11}
|
||||
{"config_id": 12, "params": {"n_steer": 5, "n_throttle": 3, "learning_rate": 0.0001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 71.8459, "run": 12}
|
||||
{"config_id": 13, "params": {"n_steer": 7, "n_throttle": 2, "learning_rate": 0.001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 87.96, "run": 13}
|
||||
{"config_id": 14, "params": {"n_steer": 7, "n_throttle": 2, "learning_rate": 0.0005, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 45.0102, "run": 14}
|
||||
{"config_id": 15, "params": {"n_steer": 7, "n_throttle": 2, "learning_rate": 0.0001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 52.6958, "run": 15}
|
||||
{"config_id": 16, "params": {"n_steer": 7, "n_throttle": 3, "learning_rate": 0.001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 80.3866, "run": 16}
|
||||
{"config_id": 17, "params": {"n_steer": 7, "n_throttle": 3, "learning_rate": 0.0005, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 84.9219, "run": 17}
|
||||
{"config_id": 18, "params": {"n_steer": 7, "n_throttle": 3, "learning_rate": 0.0001, "timesteps": 2000, "eval_episodes": 3}, "mean_reward": 77.3825, "run": 18}
|
||||
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Reference in New Issue