import os import pandas as pd import wandb.apis.public import yaml from collections import defaultdict from dataclasses import dataclass, asdict from typing import Any, Dict, Iterable, List, NamedTuple, Optional, TypeVar from urllib.parse import urlparse from runner.evaluate import Evaluation EvaluationRowSelf = TypeVar("EvaluationRowSelf", bound="EvaluationRow") @dataclass class EvaluationRow: algo: str env: str seed: Optional[int] reward_mean: float reward_std: float eval_episodes: int best: str wandb_url: str @staticmethod def data_frame(rows: List[EvaluationRowSelf]) -> pd.DataFrame: results = defaultdict(list) for r in rows: for k, v in asdict(r).items(): results[k].append(v) return pd.DataFrame(results) class EvalTableData(NamedTuple): run: wandb.apis.public.Run evaluation: Evaluation def evaluation_table(table_data: Iterable[EvalTableData]) -> str: best_stats = sorted( [d.evaluation.stats for d in table_data], key=lambda r: r.score, reverse=True )[0] table_data = sorted(table_data, key=lambda d: d.evaluation.config.seed() or 0) rows = [ EvaluationRow( config.algo, config.env_id, config.seed(), stats.score.mean, stats.score.std, len(stats), "*" if stats == best_stats else "", f"[wandb]({r.url})", ) for (r, (_, stats, config)) in table_data ] df = EvaluationRow.data_frame(rows) return df.to_markdown(index=False) def github_project_link(github_url: str) -> str: return f"[{urlparse(github_url).path}]({github_url})" def header_section(algo: str, env: str, github_url: str, wandb_report_url: str) -> str: algo_caps = algo.upper() lines = [ f"# **{algo_caps}** Agent playing **{env}**", f"This is a trained model of a **{algo_caps}** agent playing **{env}** using " f"the {github_project_link(github_url)} repo.", f"All models trained at this commit can be found at {wandb_report_url}.", ] return "\n\n".join(lines) def github_tree_link(github_url: str, commit_hash: Optional[str]) -> str: if not commit_hash: return github_project_link(github_url) return f"[{commit_hash[:7]}]({github_url}/tree/{commit_hash})" def results_section( table_data: List[EvalTableData], algo: str, github_url: str, commit_hash: str ) -> str: # type: ignore lines = [ "## Training Results", f"This model was trained from {len(table_data)} trainings of **{algo.upper()}** " + "agents using different initial seeds. " + f"These agents were trained by checking out " + f"{github_tree_link(github_url, commit_hash)}. " + "The best and last models were kept from each training. " + "This submission has loaded the best models from each training, reevaluates " + "them, and selects the best model from these latest evaluations (mean - std).", ] lines.append(evaluation_table(table_data)) return "\n\n".join(lines) def prerequisites_section() -> str: return """ ### Prerequisites: Weights & Biases (WandB) Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB. Before doing anything below, you'll need to create a wandb account and run `wandb login`. """ def usage_section(github_url: str, run_path: str, commit_hash: str) -> str: return f""" ## Usage {urlparse(github_url).path}: {github_url} Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: {github_tree_link(github_url, commit_hash)}. ``` # Downloads the model, sets hyperparameters, and runs agent for 3 episodes python enjoy.py --wandb-run-path={run_path} ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) notebook. """ def training_setion( github_url: str, commit_hash: str, algo: str, env: str, seed: Optional[int] ) -> str: return f""" ## Training If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: {github_tree_link(github_url, commit_hash)}. While training is deterministic, different hardware will give different results. ``` python train.py --algo {algo} --env {env} {'--seed ' + str(seed) if seed is not None else ''} ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) notebook. """ def benchmarking_section(report_url: str) -> str: return f""" ## Benchmarking (with Lambda Labs instance) This and other models from {report_url} were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal: ``` git clone git@github.com:sgoodfriend/rl-algo-impls.git cd rl-algo-impls bash ./lambda_labs/setup.sh wandb login bash ./lambda_labs/benchmark.sh ``` ### Alternative: Google Colab Pro+ As an alternative, [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit. """ def hyperparams_section(run_config: Dict[str, Any]) -> str: return f""" ## Hyperparameters This isn't exactly the format of hyperparams in {os.path.join("hyperparams", run_config["algo"] + ".yml")}, but instead the Wandb Run Config. However, it's very close and has some additional data: ``` {yaml.dump(run_config)} ``` """ def model_card_text( algo: str, env: str, github_url: str, commit_hash: str, wandb_report_url: str, table_data: List[EvalTableData], best_eval: EvalTableData, ) -> str: run, (_, _, config) = best_eval run_path = "/".join(run.path) return "\n\n".join( [ header_section(algo, env, github_url, wandb_report_url), results_section(table_data, algo, github_url, commit_hash), prerequisites_section(), usage_section(github_url, run_path, commit_hash), training_setion(github_url, commit_hash, algo, env, config.seed()), benchmarking_section(wandb_report_url), hyperparams_section(run.config), ] )