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>
111 lines
3.5 KiB
Python
111 lines
3.5 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 examples.atari.atari_network import (
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ActorFactoryAtariDQN,
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IntermediateModuleFactoryAtariDQNFeatures,
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)
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from examples.atari.atari_wrapper import AtariEnvFactory, AtariEpochStopCallback
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from tianshou.highlevel.config import SamplingConfig
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from tianshou.highlevel.experiment import (
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DiscreteSACExperimentBuilder,
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ExperimentConfig,
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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 DiscreteSACParams
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from tianshou.highlevel.params.policy_wrapper import (
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PolicyWrapperFactoryIntrinsicCuriosity,
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)
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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 = "PongNoFrameskip-v4",
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scale_obs: bool = False,
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buffer_size: int = 100000,
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actor_lr: float = 1e-5,
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critic_lr: float = 1e-5,
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gamma: float = 0.99,
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n_step: int = 3,
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tau: float = 0.005,
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alpha: float = 0.05,
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auto_alpha: bool = False,
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alpha_lr: float = 3e-4,
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epoch: int = 100,
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step_per_epoch: int = 100000,
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step_per_collect: int = 10,
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update_per_step: float = 0.1,
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batch_size: int = 64,
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hidden_sizes: Sequence[int] = (512,),
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training_num: int = 10,
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test_num: int = 10,
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frames_stack: int = 4,
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save_buffer_name: str | None = None, # TODO add support in high-level API?
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icm_lr_scale: float = 0.0,
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icm_reward_scale: float = 0.01,
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icm_forward_loss_weight: float = 0.2,
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) -> None:
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log_name = os.path.join(task, "sac", 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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update_per_step=update_per_step,
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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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repeat_per_collect=None,
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replay_buffer_stack_num=frames_stack,
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replay_buffer_ignore_obs_next=True,
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replay_buffer_save_only_last_obs=True,
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)
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env_factory = AtariEnvFactory(
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task,
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sampling_config.train_seed,
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sampling_config.test_seed,
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frames_stack,
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scale=scale_obs,
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)
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builder = (
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DiscreteSACExperimentBuilder(env_factory, experiment_config, sampling_config)
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.with_sac_params(
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DiscreteSACParams(
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actor_lr=actor_lr,
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critic1_lr=critic_lr,
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critic2_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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),
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)
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.with_actor_factory(ActorFactoryAtariDQN(scale_obs=False, features_only=True))
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.with_common_critic_factory_use_actor()
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.with_epoch_stop_callback(AtariEpochStopCallback(task))
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)
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if icm_lr_scale > 0:
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builder.with_policy_wrapper_factory(
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PolicyWrapperFactoryIntrinsicCuriosity(
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feature_net_factory=IntermediateModuleFactoryAtariDQNFeatures(),
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hidden_sizes=hidden_sizes,
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lr=actor_lr,
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lr_scale=icm_lr_scale,
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reward_scale=icm_reward_scale,
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forward_loss_weight=icm_forward_loss_weight,
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),
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)
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experiment = builder.build()
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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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