mirror of
https://github.com/vale981/ray
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96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
""" Example of using LinUCB on a recommendation environment with parametric
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actions. """
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import argparse
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from matplotlib import pyplot as plt
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import os
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import pandas as pd
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import time
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import ray
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from ray import tune
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from ray.tune import register_env
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from ray.rllib.env.wrappers.recsim import (
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MultiDiscreteToDiscreteActionWrapper,
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RecSimObservationBanditWrapper,
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)
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from ray.rllib.examples.env.bandit_envs_recommender_system import (
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ParametricRecSys,
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)
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# Because ParametricRecSys follows RecSim's API, we have to wrap it before
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# it can work with our Bandits agent.
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register_env(
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"ParametricRecSysEnv",
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lambda cfg: MultiDiscreteToDiscreteActionWrapper(
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RecSimObservationBanditWrapper(ParametricRecSys(**cfg))
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),
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--framework",
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choices=["tf2", "torch"],
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default="torch",
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help="The DL framework specifier.",
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)
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args = parser.parse_args()
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print(f"Running with following CLI args: {args}")
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# Temp fix to avoid OMP conflict.
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os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
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ray.init()
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config = {
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"framework": args.framework,
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"eager_tracing": (args.framework == "tf2"),
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"env": "ParametricRecSysEnv",
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"env_config": {
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"embedding_size": 20,
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"num_docs_to_select_from": 10,
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"slate_size": 1,
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"num_docs_in_db": 100,
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"num_users_in_db": 1,
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"user_time_budget": 1.0,
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},
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"num_envs_per_worker": 2, # Test with batched inference.
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"evaluation_interval": 20,
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"evaluation_duration": 100,
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"evaluation_duration_unit": "episodes",
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"simple_optimizer": True,
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}
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# Actual env timesteps per `train()` call will be
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# 10 * min_sample_timesteps_per_iteration (100 by default) = 1,000.
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training_iterations = 10
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print("Running training for %s time steps" % training_iterations)
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start_time = time.time()
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analysis = tune.run(
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"BanditLinUCB",
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config=config,
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stop={"training_iteration": training_iterations},
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num_samples=2,
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checkpoint_at_end=False,
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)
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print("The trials took", time.time() - start_time, "seconds\n")
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# Analyze cumulative regrets of the trials
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frame = pd.DataFrame()
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for key, df in analysis.trial_dataframes.items():
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frame = frame.append(df, ignore_index=True)
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x = frame.groupby("agent_timesteps_total")["episode_reward_mean"].aggregate(
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["mean", "max", "min", "std"]
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)
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plt.plot(x["mean"])
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plt.fill_between(
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x.index, x["mean"] - x["std"], x["mean"] + x["std"], color="b", alpha=0.2
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)
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plt.title("Episode reward mean")
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plt.xlabel("Training steps")
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plt.show()
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