39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import tensorflow as tf
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from recsim.simulator import environment
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from user import FlashcardUserModel
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from document import FlashcardDocumentSampler
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from recsim.simulator import recsim_gym
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from recsim.agents import full_slate_q_agent
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from recsim.simulator import runner_lib
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from agent import create_create_agent
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from util import reward, update_metrics
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slate_size = 1
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num_candidates = 10
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time_budget = 60
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tf.compat.v1.disable_eager_execution()
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create_agent_fn = create_create_agent(full_slate_q_agent.FullSlateQAgent)
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ltsenv = environment.Environment(
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FlashcardUserModel(num_candidates, time_budget, slate_size),
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FlashcardDocumentSampler(),
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num_candidates,
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slate_size,
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resample_documents=False)
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lts_gym_env = recsim_gym.RecSimGymEnv(ltsenv, reward, update_metrics)
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lts_gym_env.reset()
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tmp_base_dir = './recsim/'
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runner = runner_lib.TrainRunner(
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base_dir=tmp_base_dir,
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create_agent_fn=create_agent_fn,
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env=lts_gym_env,
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episode_log_file="",
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max_training_steps=5,
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num_iterations=1
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)
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runner.run_experiment() |