mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-19 13:32:03 +00:00
934b2acbb7
* Warn if seed doesn't return a list * Add preliminary BenchmarkRun support * Add experimental benchmark registration * Flesh out interface * Add preliminary BenchmarkRun support * Warn if seed doesn't return a list * Add experimental benchmark registration * Flesh out interface * Make benchmarkrun upload recursive * Add evaluation episodes * Add benchmark scoring * Tweak reward locations * Tweak scoring * Clear default metadata in Wrapper * Improve scoring * Expose registry; fix test * Add initial_reset_timestamp * Add back algorithm; fix tests
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
import numpy as np
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import gym
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from gym import monitoring
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from gym.monitoring.tests import helpers
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from gym.benchmarks import registration, scoring
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def test():
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benchmark = registration.Benchmark(
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id='MyBenchmark-v0',
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scorer=scoring.ClipTo01ThenAverage(),
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task_groups={
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'CartPole-v0': [{
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'seeds': 1,
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'timesteps': 5
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}, {
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'seeds': 1,
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'timesteps': 100
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}],
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})
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with helpers.tempdir() as temp:
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env = gym.make('CartPole-v0')
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env.monitor.start(temp, video_callable=False, seed=0)
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env.monitor.configure(mode='evaluation')
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rollout(env)
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env.monitor.configure(mode='training')
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for i in range(2):
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rollout(env)
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env.monitor.configure(mode='evaluation')
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rollout(env, good=True)
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env.monitor.close()
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results = monitoring.load_results(temp)
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evaluation_score = benchmark.score_evaluation('CartPole-v0', results['episode_lengths'], results['episode_rewards'], results['episode_types'], results['timestamps'], results['initial_reset_timestamp'])
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benchmark_score = benchmark.score_benchmark({
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'CartPole-v0': evaluation_score['scores'],
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})
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assert np.all(np.isclose(evaluation_score['scores'], [0.089999999999999997, 0.27000000000000002])), "evaluation_score={}".format(evaluation_score)
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assert np.isclose(benchmark_score, 0.18), "benchmark_score={}".format(benchmark_score)
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def rollout(env, good=False):
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env.reset()
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action = 0
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d = False
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while not d:
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if good:
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action = 1 - action
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o,r,d,i = env.step(action)
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