nip.scenario_base.environment.Environment#

class nip.scenario_base.environment.Environment(hyper_params: HyperParameters, settings: ExperimentSettings, dataset: Dataset, protocol_handler: ProtocolHandler, *, train: bool = True)[source]#

The base class for all Prover-Verifier RL environments.

Parameters:

hyper_params (HyperParameters) – The parameters of the experiment.
settings (ExperimentSettings) – The settings of the experiment.
dataset (Dataset) – The dataset for the environment.
protocol_handler (ProtocolHandler) – The protocol handler for the environment.
train (bool, optional) – Whether the environment is used for training or evaluation.

Methods Summary

`__init__`(hyper_params, settings, dataset, ...)
`reset`([env_state])	Reset the environment.
`step`(env_state)	Perform a step in the environment.

Attributes

`action_spec`	The specification for the action keys.
`batch_size`	The batch size of the environment.
`done_spec`	The specification for the done keys (done and terminated).
`frames_per_batch`	The number of frames to sample per training iteration.
`num_envs`	The number of batched environments.
`observation_spec`	The specification for the observation keys.
`reward_spec`	The specification for the agent reward keys.
`state_spec`	The specification for the state keys.
`steps_per_env_per_iteration`	The number of steps per batched environment in each iteration.

Methods

__init__(hyper_params: HyperParameters, settings: ExperimentSettings, dataset: Dataset, protocol_handler: ProtocolHandler, *, train: bool = True)[source]#

abstract reset(env_state: Any | None = None, **kwargs) → Any[source]#

Reset the environment.

Parameters:: env_state (Optional[Any]) – The current environment state.
Returns:: next_env_state (Any) – The reset environment state.

abstract step(env_state: Any) → Any[source]#

Perform a step in the environment.

nip.scenario_base.environment.Environment