Pre-built Environments

This module contains several pre-built experiments. Each pre-built environment is created by overriding the following components:

  • Action Handler refers to overriding of the _step() method of ConformerEnv, which determines how the molecule is modified given some action.

  • Reward Handler refers to overriding of the _reward() method of ConformerEnv, which determines how the reward is calculated based on the current configuration of the molecule.

  • Observation Handler refers to overriding of the _obs() method of ConformerEnv, which returns an observation object based on the current configuration of the molecule and is a compatible input for the neural net being used for training.

All pre-built environments inherit from ConformerEnv and share the same constructor.

class conformer_rl.environments.environments.DiscreteActionEnv(mol_config: conformer_rl.config.mol_config.MolConfig)
class conformer_rl.environments.environments.GibbsScoreEnv(mol_config: conformer_rl.config.mol_config.MolConfig)
class conformer_rl.environments.environments.GibbsScorePruningEnv(mol_config: conformer_rl.config.mol_config.MolConfig)
class conformer_rl.environments.environments.GibbsScoreEndPruningEnv(mol_config: conformer_rl.config.mol_config.MolConfig)
class conformer_rl.environments.environments.GibbsScoreLogPruningEnv(mol_config: conformer_rl.config.mol_config.MolConfig)
class conformer_rl.environments.environments.GibbsScorePruningCurriculumEnv(mol_configs: List[conformer_rl.config.mol_config.MolConfig])

Same handlers as the GibbsScorePruningEnv but with support for curriculum learning.

class conformer_rl.environments.environments.GibbsScoreLogPruningCurriculumEnv(mol_configs: List[conformer_rl.config.mol_config.MolConfig])

Same handlers as the GibbsScoreLogPruningEnv but with support for curriculum learning.