[WIP] Benchmark downloader

.gitignore

@@ -1,6 +1,9 @@
 # tensorboard runs
 runs/
 
+#benchmark folder
+download_benchmark/
+
 # videos
 #*.mp4
 notebooks/videos/*

docs/api.rst

@@ -166,6 +166,15 @@ Logging Utilities
    utils.logging.set_level
 
 
+Benchmarks
+----------------
+
+.. autosummary::
+   :toctree: generated/
+   :template: function.rst
+
+   benchmarks.benchmark_utils.download_benchmark_from_SB3_zoo
+
 Environment Wrappers
 ====================
 

docs/basics/userguide/benchmarks.md

@@ -0,0 +1,28 @@
+(benchmarks)=
+
+# How to use benchmarks
+
+Rlberry has a tool to download some benchmarks if you need to compare your agent with them.
+
+Currently, the available benchmars are :
+ - Pre-trained Reinforcement Learning agents using the rl-baselines3-zoo and Stable Baselines3 ([here](https://github.com/DLR-RM/rl-trained-agents)).
+
+## Download the benchmark
+To download the benchmark it's easy, you just have to call the function matching the expected benchmark.
+You need to specify the names of the agent and the environment. And If you want overwrite the previous data on this combination. (you can use the `output_dir` parameter if you want to download the benchmark in a specific folder).
+You can find the API about this benchmark [here](rlberry.benchmarks.benchmark_utils.download_benchmark_from_SB3_zoo)
+
+```python
+from rlberry.benchmarks.benchmark_utils import download_benchmark_from_SB3_zoo
+
+agent_name = "dqn"
+environment_name = "PongNoFrameskip-v4_1"
+
+path_with_downloaded_files = download_benchmark_from_SB3_zoo(
+    agent_name, environment_name, overwrite=True
+)
+```
+
+## How to use these benchmarks to compare your agent
+
+in construction ...

docs/changelog.rst

@@ -7,7 +7,12 @@ Changelog
 Dev version
 -----------
 
-* nothing
+
+
+*PR #449*
+
+  * Add module to download benchmarks
+    - Pre-trained Reinforcement Learning agents using the rl-baselines3-zoo and Stable Baselines3.
 
 Version 0.7.1
 -------------

docs/user_guide.md

@@ -47,3 +47,4 @@ You can find more details about installation [here](installation)!
 - Transfer Learning (In construction)
 - [Hypothesis testing for comparison of RL agents](comparison_page)
 - [Adaptive hypothesis testing for comparison of RL agents with AdaStop](adastop_userguide)
+- [Using benchmarks](benchmarks)

rlberry/benchmarks/benchmark_utils.py

@@ -0,0 +1,151 @@
+import requests
+from tempfile import mkdtemp
+import os
+import shutil
+
+
+# # TODO : convert external benchmark to DataFrame that match the input of rlberry.manager.comparaison.py -> compare_agents_data()
+# # TODO : Download the external benchmark to a specific folder (or new rlberrygithub?), except if they are stable (huggingface/github)
+
+# benchmark_list = {
+#     "Google Atari bucket": "https://console.cloud.google.com/storage/brow",
+#     "SB3 zoo": "https://github.com/DLR-RM/rl-baselines3-zoo/tree/master/logs/benchmark",
+#     "cleanrl": "https://wandb.ai/openrlbenchmark/openrlbenchmark/reportlist",
+# }
+
+
+# def import_from_google_atari_bucket():
+#     """import benchmark from Google Atari bucket
+
+#     Parameters
+#     -----------
+#     x_vec : numpy.ndarray
+#         numpy 1d array to be searched in the bins
+#     bins : list
+#         list of numpy 1d array, bins[d] = bins of the d-th dimension
+
+
+#     Returns
+#     --------
+#     index (int) corresponding to the position of x in the partition
+#     defined by the bins.
+#     """
+#     print("TODO")
+
+
+# def import_from_cleanrl():
+#     print("TODO")
+
+
+# def import_from_hugingface():
+#     print("TODO")
+
+
+def download_benchmark_from_SB3_zoo(
+    agent_name, environment_name, overwrite, output_dir=None
+):
+    """
+    Download folder from pre-trained Reinforcement Learning agents using the rl-baselines3-zoo and Stable Baselines3.
+    https://github.com/DLR-RM/rl-trained-agents
+
+    Parameters
+    -----------
+    agent_name : str
+        agent name for benchmark to download
+    environment_name : list
+        environment name for benchmark to download
+    overwrite : bool
+        how to manage if the combination agent_name/environment_name exist :
+        True : delete the previous folder, then download
+        False : raise an error
+    output_dir : str
+        root path where to download files. (default=None : create temp folder)
+
+    Returns
+    --------
+    Return the path containing the downloaded files   (output_dir/agent_name/environment_name)
+    """
+    if not output_dir:
+        output_dir = mkdtemp()
+
+    GITHUB_URL = "https://raw.githubusercontent.com/DLR-RM/rl-trained-agents/master/"
+    base_url = GITHUB_URL + agent_name + "/" + environment_name + "/"
+
+    output_folder = os.path.join(output_dir, agent_name, environment_name)
+    environment_base_name = environment_name.split("_")[0]
+
+    if os.path.exists(output_folder):
+        if not overwrite:
+            raise FileExistsError(
+                "The 'overwrite' bool is false, and the combination %s / %s already exist"
+                % (agent_name, environment_name)
+            )
+        shutil.rmtree(output_folder)
+    os.makedirs(output_folder)
+
+    # download CSVs
+    url_content = None
+    i = 0
+    while url_content != b"404: Not Found":
+        file_name_to_download = str(i) + ".monitor.csv"
+        url_csv_to_download = base_url + file_name_to_download
+
+        req = requests.get(url_csv_to_download)
+        url_content = req.content
+
+        if url_content != b"404: Not Found":
+            csv_file = open(os.path.join(output_folder, file_name_to_download), "wb")
+            csv_file.write(url_content)
+            csv_file.close()
+        else:
+            break
+        i = i + 1
+
+    # download zip
+    file_name_to_download = environment_base_name + ".zip"
+    url_zip_to_download = base_url + file_name_to_download
+    req = requests.get(url_zip_to_download)
+    url_content = req.content
+    csv_file = open(os.path.join(output_folder, file_name_to_download), "wb")
+    csv_file.write(url_content)
+    csv_file.close()
+
+    # download evaluations.npz
+    file_name_to_download = "evaluations.npz"
+    url_zip_to_download = base_url + file_name_to_download
+    req = requests.get(url_zip_to_download)
+    url_content = req.content
+    csv_file = open(os.path.join(output_folder, file_name_to_download), "wb")
+    csv_file.write(url_content)
+    csv_file.close()
+
+    # hyperparameter and config
+    config_folder = output_folder + "/" + environment_base_name
+    base_url_config = base_url + environment_base_name + "/"
+
+    os.makedirs(config_folder)
+    file_name_to_download = "args.yml"
+    url_zip_to_download = base_url_config + file_name_to_download
+    req = requests.get(url_zip_to_download)
+    url_content = req.content
+    csv_file = open(os.path.join(config_folder, file_name_to_download), "wb")
+    csv_file.write(url_content)
+    csv_file.close()
+
+    file_name_to_download = "config.yml"
+    url_zip_to_download = base_url_config + file_name_to_download
+    req = requests.get(url_zip_to_download)
+    url_content = req.content
+    csv_file = open(os.path.join(config_folder, file_name_to_download), "wb")
+    csv_file.write(url_content)
+    csv_file.close()
+
+    file_name_to_download = "vecnormalize.pkl"
+    url_zip_to_download = base_url_config + file_name_to_download
+    req = requests.get(url_zip_to_download)
+    url_content = req.content
+    csv_file = open(os.path.join(config_folder, file_name_to_download), "wb")
+    csv_file.write(url_content)
+    csv_file.close()
+
+    return output_folder

rlberry/benchmarks/tests/test_benchmark_utils.py

@@ -0,0 +1,88 @@
+import os
+import shutil
+from rlberry.benchmarks.benchmark_utils import download_benchmark_from_SB3_zoo
+import pytest
+
+
+@pytest.mark.parametrize("agent_class", ["dqn"])
+@pytest.mark.parametrize("env", ["PongNoFrameskip-v4_1"])
+def test_download_benchmark_from_SB3_zoo_(agent_class, env):
+    # remove previous test if existing
+    test_folder_path = "./tests_dl"
+    if os.path.exists(test_folder_path):
+        shutil.rmtree(test_folder_path)
+    os.makedirs(test_folder_path)
+
+    # download benchmark
+    ret_value = download_benchmark_from_SB3_zoo(
+        agent_class, env, overwrite=True, output_dir=test_folder_path
+    )
+
+    # tests expected result
+    environment_base_name = env.split("_")[0]
+    assert str(os.path.join(test_folder_path, agent_class, env)) == ret_value
+    assert os.path.exists(os.path.join(test_folder_path, agent_class, env))
+    assert os.path.exists(
+        os.path.join(test_folder_path, agent_class, env, "0.monitor.csv")
+    )
+    assert os.path.exists(
+        os.path.join(test_folder_path, agent_class, env, environment_base_name + ".zip")
+    )
+    assert os.path.exists(
+        os.path.join(test_folder_path, agent_class, env, "evaluations.npz")
+    )
+    assert os.path.exists(
+        os.path.join(
+            test_folder_path, agent_class, env, environment_base_name, "args.yml"
+        )
+    )
+    assert os.path.exists(
+        os.path.join(
+            test_folder_path, agent_class, env, environment_base_name, "config.yml"
+        )
+    )
+    assert os.path.exists(
+        os.path.join(
+            test_folder_path,
+            agent_class,
+            env,
+            environment_base_name,
+            "vecnormalize.pkl",
+        )
+    )
+
+    if os.path.exists(test_folder_path):
+        shutil.rmtree(test_folder_path)
+
+
+@pytest.mark.parametrize("agent_class", ["dqn"])
+@pytest.mark.parametrize("env", ["PongNoFrameskip-v4_1"])
+@pytest.mark.parametrize("overwrite", [True, False])
+def test_download_benchmark_from_SB3_zoo_overwrite_True(agent_class, env, overwrite):
+    # remove previous test if existing
+    test_folder_path = "./tests_dl"
+    if os.path.exists(test_folder_path):
+        shutil.rmtree(test_folder_path)
+    os.makedirs(test_folder_path)
+
+    # first call
+    ret_value = download_benchmark_from_SB3_zoo(
+        agent_class, env, overwrite=overwrite, output_dir=test_folder_path
+    )
+
+    #'overwrite' test
+    error_was_raised = False
+    try:
+        ret_value = download_benchmark_from_SB3_zoo(
+            agent_class, env, overwrite=overwrite, output_dir=test_folder_path
+        )
+    except FileExistsError:
+        error_was_raised = True
+
+    if overwrite:
+        assert not error_was_raised
+    else:
+        assert error_was_raised
+
+    if os.path.exists(test_folder_path):
+        shutil.rmtree(test_folder_path)