entry_point="metabo.environment.metabo_gym:MetaBO",
max_episode_steps=env_spec["T"],
reward_threshold=None,
kwargs=env_spec)
# define evaluation run
eval_spec = {
"env_id": env_spec["env_id"],
"env_seed_offset": 100,
"policy": af,
"logpath": logpath,
"load_iter": load_iter,
"deterministic": deterministic,
"policy_specs": policy_specs,
"savepath": savepath,
"n_workers": n_workers,
"n_episodes": n_episodes,
"T": env_spec["T"],
}
# perform evaluation
print("Evaluating {} on {}...".format(af, env_spec["env_id"]))
eval_experiment(eval_spec)
print("Done! Saved result in {}".format(savepath))
print("**********************\n\n")
# plot (plot is saved to savepath)
print("Plotting...")
plot_results(path=savepath, logplot=True)
print("Done! Saved plot in {}".format(savepath))
entry_point="metabo.environment.metabo_gym:MetaBO",
max_episode_steps=env_spec["T"],
reward_threshold=None,
kwargs=env_spec)
# define evaluation run
eval_spec = {
"env_id": env_spec["env_id"],
"env_seed_offset": 100,
"policy": af,
"logpath": logpath,
"load_iter": load_iter,
"deterministic": deterministic,
"policy_specs": policy_specs,
"savepath": savepath,
"n_workers": n_workers,
"n_episodes": n_episodes,
"T": env_spec["T"],
}
# perform evaluation
print("Evaluating {} on {}...".format(af, env_spec["env_id"]))
eval_experiment(eval_spec)
print("Done! Saved result in {}".format(savepath))
print("**********************\n\n")
# plot (plot is saved to savepath)
print("Plotting...")
plot_results(path=savepath, logplot=False)
print("Done! Saved plot in {}".format(savepath))