Log full experiment configuration, adding string representations to relevant classes

examples/atari/atari_ppo_hl.py

@@ -21,6 +21,7 @@ from tianshou.highlevel.params.policy_params import PPOParams
 from tianshou.highlevel.params.policy_wrapper import (
     PolicyWrapperFactoryIntrinsicCuriosity,
 )
+from tianshou.utils import logging
 
 
 def main(
@@ -114,4 +115,4 @@ def main(
 
 
 if __name__ == "__main__":
-    CLI(main)
+    logging.run_main(lambda: CLI(main))

examples/mujoco/mujoco_a2c_hl.py

@@ -16,6 +16,7 @@ from tianshou.highlevel.experiment import (
 from tianshou.highlevel.optim import OptimizerFactoryRMSprop
 from tianshou.highlevel.params.lr_scheduler import LRSchedulerFactoryLinear
 from tianshou.highlevel.params.policy_params import A2CParams
+from tianshou.utils import logging
 
 
 def main(
@@ -82,4 +83,4 @@ def main(
 
 
 if __name__ == "__main__":
-    CLI(main)
+    logging.run_main(lambda: CLI(main))

examples/mujoco/mujoco_ddpg_hl.py

@@ -14,6 +14,7 @@ from tianshou.highlevel.experiment import (
 )
 from tianshou.highlevel.params.noise import MaxActionScaledGaussian
 from tianshou.highlevel.params.policy_params import DDPGParams
+from tianshou.utils import logging
 
 
 def main(
@@ -75,4 +76,4 @@ def main(
 
 
 if __name__ == "__main__":
-    CLI(main)
+    logging.run_main(lambda: CLI(main))

examples/mujoco/mujoco_ppo_hl.py

@@ -16,6 +16,7 @@ from tianshou.highlevel.experiment import (
 )
 from tianshou.highlevel.params.lr_scheduler import LRSchedulerFactoryLinear
 from tianshou.highlevel.params.policy_params import PPOParams
+from tianshou.utils import logging
 
 
 def main(
@@ -95,4 +96,4 @@ def main(
 
 
 if __name__ == "__main__":
-    CLI(main)
+    logging.run_main(lambda: CLI(main))

examples/mujoco/mujoco_sac_hl.py

@@ -14,6 +14,7 @@ from tianshou.highlevel.experiment import (
 )
 from tianshou.highlevel.params.alpha import AutoAlphaFactoryDefault
 from tianshou.highlevel.params.policy_params import SACParams
+from tianshou.utils import logging
 
 
 def main(
@@ -81,4 +82,4 @@ def main(
 
 
 if __name__ == "__main__":
-    CLI(main)
+    logging.run_main(lambda: CLI(main))

examples/mujoco/mujoco_td3_hl.py

@@ -17,6 +17,7 @@ from tianshou.highlevel.params.noise import (
     MaxActionScaledGaussian,
 )
 from tianshou.highlevel.params.policy_params import TD3Params
+from tianshou.utils import logging
 
 
 def main(
@@ -84,4 +85,4 @@ def main(
 
 
 if __name__ == "__main__":
-    CLI(main)
+    logging.run_main(lambda: CLI(main))

tianshou/highlevel/agent.py

@@ -41,6 +41,7 @@ from tianshou.policy import (
 from tianshou.trainer import BaseTrainer, OffpolicyTrainer, OnpolicyTrainer
 from tianshou.utils.net import continuous, discrete
 from tianshou.utils.net.common import ActorCritic
+from tianshou.utils.string import ToStringMixin
 
 CHECKPOINT_DICT_KEY_MODEL = "model"
 CHECKPOINT_DICT_KEY_OBS_RMS = "obs_rms"
@@ -48,7 +49,7 @@ TParams = TypeVar("TParams", bound=Params)
 TPolicy = TypeVar("TPolicy", bound=BasePolicy)
 
 
-class AgentFactory(ABC):
+class AgentFactory(ABC, ToStringMixin):
     def __init__(self, sampling_config: RLSamplingConfig, optim_factory: OptimizerFactory):
         self.sampling_config = sampling_config
         self.optim_factory = optim_factory

tianshou/highlevel/experiment.py

@@ -1,3 +1,4 @@
+import logging
 from abc import abstractmethod
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass
@@ -37,7 +38,9 @@ from tianshou.highlevel.params.policy_wrapper import PolicyWrapperFactory
 from tianshou.highlevel.persistence import PersistableConfigProtocol
 from tianshou.policy import BasePolicy
 from tianshou.trainer import BaseTrainer
+from tianshou.utils.string import ToStringMixin
 
+log = logging.getLogger(__name__)
 TPolicy = TypeVar("TPolicy", bound=BasePolicy)
 TTrainer = TypeVar("TTrainer", bound=BaseTrainer)
 
@@ -59,7 +62,7 @@ class RLExperimentConfig:
     watch_num_episodes = 10
 
 
-class RLExperiment(Generic[TPolicy, TTrainer]):
+class RLExperiment(Generic[TPolicy, TTrainer], ToStringMixin):
     def __init__(
         self,
         config: RLExperimentConfig,
@@ -204,13 +207,15 @@ class RLExperimentBuilder:
         agent_factory = self._create_agent_factory()
         if self._policy_wrapper_factory:
             agent_factory.set_policy_wrapper_factory(self._policy_wrapper_factory)
-        return RLExperiment(
+        experiment = RLExperiment(
             self._config,
             self._env_factory,
             agent_factory,
             self._logger_factory,
             env_config=self._env_config,
         )
+        log.info(f"Created experiment:\n{experiment.pprints()}")
+        return experiment
 
 
 class _BuilderMixinActorFactory:

tianshou/highlevel/logger.py

@@ -6,6 +6,7 @@ from typing import Literal, TypeAlias
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.utils import TensorboardLogger, WandbLogger
+from tianshou.utils.string import ToStringMixin
 
 TLogger: TypeAlias = TensorboardLogger | WandbLogger
 
@@ -16,7 +17,7 @@ class Logger:
     log_path: str
 
 
-class LoggerFactory(ABC):
+class LoggerFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_logger(self, log_name: str, run_id: int | None, config_dict: dict) -> Logger:
         pass

tianshou/highlevel/module/actor.py

@@ -8,6 +8,7 @@ from tianshou.highlevel.env import Environments, EnvType
 from tianshou.highlevel.module.core import TDevice, init_linear_orthogonal
 from tianshou.utils.net import continuous, discrete
 from tianshou.utils.net.common import BaseActor, Net
+from tianshou.utils.string import ToStringMixin
 
 
 class ContinuousActorType:
@@ -15,7 +16,7 @@ class ContinuousActorType:
     DETERMINISTIC = "deterministic"
 
 
-class ActorFactory(ABC):
+class ActorFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_module(self, envs: Environments, device: TDevice) -> BaseActor:
         pass

tianshou/highlevel/module/critic.py

@@ -7,9 +7,10 @@ from tianshou.highlevel.env import Environments, EnvType
 from tianshou.highlevel.module.core import TDevice, init_linear_orthogonal
 from tianshou.utils.net import continuous
 from tianshou.utils.net.common import Net
+from tianshou.utils.string import ToStringMixin
 
 
-class CriticFactory(ABC):
+class CriticFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_module(self, envs: Environments, device: TDevice, use_action: bool) -> nn.Module:
         pass

tianshou/highlevel/module/module_opt.py

@@ -8,6 +8,7 @@ from tianshou.highlevel.module.core import TDevice
 from tianshou.highlevel.module.critic import CriticFactory
 from tianshou.highlevel.optim import OptimizerFactory
 from tianshou.utils.net.common import ActorCritic
+from tianshou.utils.string import ToStringMixin
 
 
 @dataclass
@@ -30,7 +31,7 @@ class ActorCriticModuleOpt:
         return self.actor_critic_module.critic
 
 
-class ActorModuleOptFactory:
+class ActorModuleOptFactory(ToStringMixin):
     def __init__(self, actor_factory: ActorFactory, optim_factory: OptimizerFactory):
         self.actor_factory = actor_factory
         self.optim_factory = optim_factory
@@ -41,7 +42,7 @@ class ActorModuleOptFactory:
         return ModuleOpt(actor, opt)
 
 
-class CriticModuleOptFactory:
+class CriticModuleOptFactory(ToStringMixin):
     def __init__(
         self,
         critic_factory: CriticFactory,

tianshou/highlevel/optim.py

@@ -4,8 +4,10 @@ from typing import Any
 import torch
 from torch.optim import Adam, RMSprop
 
+from tianshou.utils.string import ToStringMixin
 
-class OptimizerFactory(ABC):
+
+class OptimizerFactory(ABC, ToStringMixin):
     # TODO: Is it OK to assume that all optimizers have a learning rate argument?
     # Right now, the learning rate is typically a configuration parameter.
     # If we drop the assumption, we can't have that and will need to move the parameter