ade85ab32b
# Changes ## Dependencies - New extra "eval" ## Api Extension - `Experiment` and `ExperimentConfig` now have a `name`, that can however be overridden when `Experiment.run()` is called - When building an `Experiment` from an `ExperimentConfig`, the user has the option to add info about seeds to the name. - New method in `ExperimentConfig` called `build_default_seeded_experiments` - `SamplingConfig` has an explicit training seed, `test_seed` is inferred. - New `evaluation` package for repeating the same experiment with multiple seeds and aggregating the results (important extension!). Currently in alpha state. - Loggers can now restore the logged data into python by using the new `restore_logged_data` ## Breaking Changes - `AtariEnvFactory` (in examples) now receives explicit train and test seeds - `EnvFactoryRegistered` now requires an explicit `test_seed` - `BaseLogger.prepare_dict_for_logging` is now abstract --------- Co-authored-by: Maximilian Huettenrauch <m.huettenrauch@appliedai.de> Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de> Co-authored-by: Michael Panchenko <35432522+MischaPanch@users.noreply.github.com>
91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
#!/usr/bin/env python3
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import os
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from collections.abc import Sequence
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from typing import Literal
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from examples.mujoco.mujoco_env import MujocoEnvFactory
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from tianshou.highlevel.config import SamplingConfig
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from tianshou.highlevel.experiment import (
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ExperimentConfig,
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REDQExperimentBuilder,
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)
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from tianshou.highlevel.params.alpha import AutoAlphaFactoryDefault
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from tianshou.highlevel.params.policy_params import REDQParams
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from tianshou.utils import logging
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from tianshou.utils.logging import datetime_tag
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def main(
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experiment_config: ExperimentConfig,
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task: str = "Ant-v4",
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buffer_size: int = 1000000,
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hidden_sizes: Sequence[int] = (256, 256),
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ensemble_size: int = 10,
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subset_size: int = 2,
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actor_lr: float = 1e-3,
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critic_lr: float = 1e-3,
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gamma: float = 0.99,
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tau: float = 0.005,
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alpha: float = 0.2,
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auto_alpha: bool = False,
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alpha_lr: float = 3e-4,
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start_timesteps: int = 10000,
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epoch: int = 200,
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step_per_epoch: int = 5000,
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step_per_collect: int = 1,
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update_per_step: int = 20,
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n_step: int = 1,
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batch_size: int = 256,
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target_mode: Literal["mean", "min"] = "min",
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training_num: int = 1,
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test_num: int = 10,
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) -> None:
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log_name = os.path.join(task, "redq", str(experiment_config.seed), datetime_tag())
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sampling_config = SamplingConfig(
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num_epochs=epoch,
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step_per_epoch=step_per_epoch,
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batch_size=batch_size,
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num_train_envs=training_num,
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num_test_envs=test_num,
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buffer_size=buffer_size,
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step_per_collect=step_per_collect,
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update_per_step=update_per_step,
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repeat_per_collect=None,
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start_timesteps=start_timesteps,
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start_timesteps_random=True,
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)
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env_factory = MujocoEnvFactory(
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task,
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train_seed=sampling_config.train_seed,
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test_seed=sampling_config.test_seed,
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obs_norm=False,
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)
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experiment = (
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REDQExperimentBuilder(env_factory, experiment_config, sampling_config)
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.with_redq_params(
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REDQParams(
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actor_lr=actor_lr,
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critic_lr=critic_lr,
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gamma=gamma,
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tau=tau,
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alpha=AutoAlphaFactoryDefault(lr=alpha_lr) if auto_alpha else alpha,
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estimation_step=n_step,
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target_mode=target_mode,
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subset_size=subset_size,
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ensemble_size=ensemble_size,
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),
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)
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.with_actor_factory_default(hidden_sizes)
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.with_critic_ensemble_factory_default(hidden_sizes)
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.build()
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)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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logging.run_cli(main)
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