nip.scenario_base.agents.AgentPart#

class nip.scenario_base.agents.AgentPart(hyper_params: HyperParameters, settings: ExperimentSettings, agent_name: str, protocol_handler: ProtocolHandler)[source]#

Base class for all agent parts: bodies and heads.

The in and out keys are split into agent-level and environment-level keys. Agent-level keys are nested under “agents” in the environment’s state dict, while environment-level keys are at the top level.

Parameters:

• hyper_params (HyperParameters) – The parameters of the experiment.

• settings (ExperimentSettings) – The settings of the experiment.

• agent_name (str) – The name of the agent.

• protocol_handler (ProtocolHandler) – The protocol handler for the experiment.

• device (TorchDevice, optional) – The device to use for this agent part. If not given, the CPU is used.

• attributes (Class)

• ----------------

• agent_level_in_keys (Iterable[NestedKey]) – The keys required by the agent part whose values are per-agent (so in the environment’s state dict will be nested under “agents”).

• env_level_in_keys (Iterable[NestedKey]) – The keys required by the agent part whose values are per-environment (so in the environment’s state dict will be at the top level).

• agent_level_out_keys (Iterable[NestedKey]) – The keys produced by the agent part whose values are per-agent (so in the environment’s state dict will be nested under “agents”).

• env_level_out_keys (Iterable[NestedKey]) – The keys produced by the agent part whose values are per-environment (so in the environment’s state dict will be at the top level).

Methods Summary

__init__(hyper_params, settings, agent_name, ...)
forward(data)	Forward pass through the agent part.
get_state_dict()	Get the state of the agent part as a dict.
set_state(checkpoint)	Set the state of the agent from a checkpoint.

Attributes

agent_id	The ID of the agent.
agent_level_in_keys
agent_level_out_keys
env_level_in_keys
env_level_out_keys
in_keys	The keys required by the module.
is_prover	Whether the agent is a prover.
is_verifier	Whether the agent is a verifier.
max_message_rounds	The maximum number of message rounds in the protocol.
num_visible_message_channels	The number of message channels visible to the agent.
out_keys	The keys produced by the module.
required_pretrained_models	The pretrained models used by the agent.
visible_message_channel_indices	The indices of the message channels visible to the agent.
visible_message_channel_mask	The mask for the message channels visible to the agent.
visible_message_channel_names	The names of the message channels visible to the agent.

Methods

__init__(hyper_params: HyperParameters, settings: ExperimentSettings, agent_name: str, protocol_handler: ProtocolHandler)[source]#

abstract forward(data: Any) → Any[source]#

Forward pass through the agent part.

Parameters:

data (Any) – The input to the agent part.

Returns:

output (Any) – The output of the forward pass on the input.

get_state_dict() → dict[source]#

Get the state of the agent part as a dict.

This method should be implemented by subclasses capable of saving their state.

Returns:

state_dict (dict) – The state of the agent part.

set_state(checkpoint: AgentState)[source]#

Set the state of the agent from a checkpoint.

This method should be overridden by subclasses to restore the state of the agent from a checkpoint.

Parameters:

checkpoint (AgentCheckpoint) – The checkpoint to restore the state from.