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>
97 lines
3.0 KiB
Python
97 lines
3.0 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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import torch
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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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TRPOExperimentBuilder,
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)
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from tianshou.highlevel.params.dist_fn import (
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DistributionFunctionFactoryIndependentGaussians,
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)
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from tianshou.highlevel.params.lr_scheduler import LRSchedulerFactoryLinear
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from tianshou.highlevel.params.policy_params import TRPOParams
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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 = 4096,
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hidden_sizes: Sequence[int] = (64, 64),
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lr: float = 1e-3,
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gamma: float = 0.99,
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epoch: int = 100,
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step_per_epoch: int = 30000,
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step_per_collect: int = 1024,
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repeat_per_collect: int = 1,
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batch_size: int = 16,
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training_num: int = 16,
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test_num: int = 10,
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rew_norm: bool = True,
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gae_lambda: float = 0.95,
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bound_action_method: Literal["clip", "tanh"] = "clip",
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lr_decay: bool = True,
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norm_adv: bool = True,
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optim_critic_iters: int = 20,
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max_kl: float = 0.01,
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backtrack_coeff: float = 0.8,
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max_backtracks: int = 10,
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) -> None:
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log_name = os.path.join(task, "trpo", 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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repeat_per_collect=repeat_per_collect,
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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=True,
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)
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experiment = (
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TRPOExperimentBuilder(env_factory, experiment_config, sampling_config)
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.with_trpo_params(
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TRPOParams(
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discount_factor=gamma,
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gae_lambda=gae_lambda,
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action_bound_method=bound_action_method,
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reward_normalization=rew_norm,
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advantage_normalization=norm_adv,
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optim_critic_iters=optim_critic_iters,
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max_kl=max_kl,
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backtrack_coeff=backtrack_coeff,
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max_backtracks=max_backtracks,
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lr=lr,
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lr_scheduler_factory=LRSchedulerFactoryLinear(sampling_config)
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if lr_decay
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else None,
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dist_fn=DistributionFunctionFactoryIndependentGaussians(),
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),
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)
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.with_actor_factory_default(hidden_sizes, torch.nn.Tanh, continuous_unbounded=True)
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.with_critic_factory_default(hidden_sizes, torch.nn.Tanh)
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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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