VPG playing PongNoFrameskip-v4 from https://github.com/sgoodfriend/rl-algo-impls/tree/e8bc541d8b5e67bb4d3f2075282463fb61f5f2c6
10ca8cb
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") | |
class EvaluationRow: | |
algo: str | |
env: str | |
seed: Optional[int] | |
reward_mean: float | |
reward_std: float | |
eval_episodes: int | |
best: str | |
wandb_url: str | |
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), | |
] | |
) | |