nip.protocols.main_protocols.MultiChannelTestProtocol

nip.protocols.main_protocols.MultiChannelTestProtocol#

class nip.protocols.main_protocols.MultiChannelTestProtocol(hyper_params: HyperParameters, settings: ExperimentSettings, *, verifier_name: str = 'verifier')[source]#

A protocol for testing multi-channel communication between agents.

Methods Summary

__init__(hyper_params, settings, *[, ...])

_get_agent_decision_made_mask(round_id, y, ...)

Get a mask indicating whether an agent has made a decision.

_get_new_terminated_mask(round_id, ...)

Get a mask indicating whether the episode has been newly terminated.

_get_verifier_guess_reward_continuous(...)

Compute the guess reward for the verifier, with continuous_decision.

_get_verifier_guess_reward_discrete(reward, ...)

Compute the guess reward for the verifier, without continuous_decision.

_include_prover_rewards(...)

Compute the rewards for the other agents and add them to the current reward.

_validate_active_agents()

Make sure that agents are only active in channels they can see.

can_agent_be_active(agent_name, round_id, ...)

Specify whether an agent can be active in a given round and channel.

can_agent_be_active_any_channel(agent_name, ...)

Specify whether an agent can be active in any channel in a given round.

can_agent_see_channel(agent_name, channel_name)

Determine whether an agent can see a channel.

get_active_agents_mask_from_rounds_and_seed(...)

Get a boolean mask of active agents for a batch of rounds.

get_agent_visible_channels(agent_name)

Get the names of the channels visible to an agent.

get_verifier_guess_mask_from_rounds_and_seed(...)

Get a boolean mask indicating when the verifier can make a guess.

is_agent_active(agent_name, round_id, ...)

Specify whether an agent is active in a given round and channel.

max_reward(agent_name)

Get the maximum possible reward for an agent.

min_reward(agent_name)

Get the minimum possible reward for an agent.

reward_mid_point_estimate(agent_name)

Get an estimate of the expected reward if all agents play randomly.

step_interaction_protocol(env_td)

Take a step in the interaction protocol.

Attributes

active_agents_by_round

A list of which agent names are active in each round and channel.

active_agents_mask

A boolean mask indicating which agents are active in each round and channel.

agent_channel_visibility

agent_channel_visibility_mask

A boolean mask indicating which agents can see which message channels.

agent_first_active_round

The first round in which each agent is or can be active.

agent_names

can_be_zero_knowledge

default_stackelberg_sequence

The default Stackelberg sequence for the protocol.

max_message_rounds

max_verifier_questions

message_channel_names

min_message_rounds

num_agents

The number of agents in the protocol.

num_message_channels

The number of message channels in the protocol.

protocol_common

The common protocol parameters.

prover_indices

The indices of the provers in the list of agent names.

prover_names

The names of the provers in the protocol.

stackelberg_sequence

The actual Stackelberg sequence used in this experiment.

verifier_index

The index of the verifier in the list of agent names.

verifier_names

The names of the verifiers in the protocol.

verifier_neither_accept_nor_reject_reward

The reward for the verifier when they neither accept nor reject.

Methods

__init__(hyper_params: HyperParameters, settings: ExperimentSettings, *, verifier_name: str = 'verifier')[source]#
_get_agent_decision_made_mask(round_id: Int[Tensor, '...'], y: Int[Tensor, '...'], guess_mask: Bool[Tensor, '...'], decision: Int[Tensor, '...'], *, follow_force_guess: bool = True) → Bool[Tensor, '...'][source]#

Get a mask indicating whether an agent has made a decision.

Parameters:

  • round_id (Int[Tensor, "..."]) – The round number.

  • y (Int[Tensor, "..."]) – The target value.

  • guess_mask (Bool[Tensor, "..."]) – A mask indicating whether the agent is allowed to make a guess.

  • decision (Int[Tensor, "..."]) – The decision output of the agent.

  • follow_force_guess (bool, default=True) – Whether to follow the force_guess parameter, which forces the agent to make a certain decision.

Returns:

decision_made (Bool[Tensor, “…”]) – A mask indicating whether the agent has made a decision.

_get_new_terminated_mask(round_id: Int[Tensor, '...'], verifier_decision_made: Bool[Tensor, '...']) → Bool[Tensor, '...'][source]#

Get a mask indicating whether the episode has been newly terminated.

“Newly terminated” means that the episode has been terminated this round. This happens when the max number of rounds has been reached and the verifier has not guessed.

Parameters:

  • round_id (Int[Tensor, "..."]) – The round number.

  • verifier_decision_made (Bool[Tensor, "..."]) – A mask indicating whether the verifier has made a decision.

Returns:

terminated (Bool[Tensor, “…”]) – A mask indicating whether the episode has been newly terminated.

_get_verifier_guess_reward_continuous(reward: Float[Tensor, '... agent'], y: Int[Tensor, '...'], verifier_decision_made: Bool[Tensor, '...'], verifier_float_decision: Float[Tensor, '...']) → Float[Tensor, '... agent'][source]#

Compute the guess reward for the verifier, with continuous_decision.

This computes the reward for the verifier when they make a guess, when the more fine-grained decision is used.

Parameters:

  • reward (Float[Tensor, "... agent"]) – The tensor of rewards for the agents, which is updated in place.

  • y (Int[Tensor, "..."]) – The target value.

  • verifier_decision_made (Bool[Tensor, "..."]) – A mask indicating whether the verifier has made a decision.

  • verifier_float_decision (Float[Tensor, "..."]) – The verifier’s (continuous) decision.

_get_verifier_guess_reward_discrete(reward: Float[Tensor, '... agent'], y: Int[Tensor, '...'], verifier_decision_made: Bool[Tensor, '...'], verifier_decision: Int[Tensor, '...']) → Float[Tensor, '... agent'][source]#

Compute the guess reward for the verifier, without continuous_decision.

This computes the reward for the verifier when they make a guess, when the more fine-grained decision is not used.

Parameters:

  • reward (Float[Tensor, "... agent"]) – The tensor of rewards for the agents, which is updated in place.

  • y (Int[Tensor, "..."]) – The target value.

  • verifier_decision_made (Bool[Tensor, "..."]) – A mask indicating whether the verifier has made a decision.

  • verifier_decision (Int[Tensor, "..."]) –

    The verifier’s (discrete) decision. This has the following possible values:

    • 0: reject

    • 1: accept

    • 2: no decision

    • 3: end with neither accept nor reject (only relevant for text-based scenarios)

_include_prover_rewards(verifier_decision_made: Bool[Tensor, '...'], verifier_decision: Int[Tensor, '...'], verifier_float_decision: Float[Tensor, '...'] | None, reward: Float[Tensor, '... agent'], env_td: TensorDictBase | NestedArrayDict)[source]#

Compute the rewards for the other agents and add them to the current reward.

The default implementation is as follows:

  • If there is one prover, they are rewarded when the verifier guesses “accept”.

  • If there are two provers, the first is rewarded when the verifier guesses “reject” and the second is rewarded when the verifier guesses “accept”.

When the continuous_decision key is present in the environment tensor, a continuous version of this is used instead, where the reward is a linear transformation of the verifier’s decision.

Implement a custom method for protocols with more than two provers, or for protocols with different reward schemes.

The reward tensor is updated in place, adding in the rewards for the agents at the appropriate indices.

Parameters:

  • verifier_decision_made (Bool[Tensor, "..."]) – A boolean mask indicating whether the verifier has made a decision.

  • verifier_decision (Int[Tensor, "..."]) – The verifier’s discrete decision.

  • verifier_float_decision (Float[Tensor, "..."] | None) – The verifier’s continuous decision. This is only used if the continuous_decision key is present in the environment tensor. If not None, it is used to compute the reward for the provers instead of the discrete decision.

  • reward (Float[Tensor, "... agent"]) – The currently computed reward, which should include the reward for the verifier. This is updated in place.

  • env_td (TensorDictBase | NestedArrayDict) – The current observation and state. If a NestedArrayDict, it is converted to a TensorDictBase.

_validate_active_agents()[source]#

Make sure that agents are only active in channels they can see.

can_agent_be_active(agent_name: str, round_id: int, channel_name: str) → bool[source]#

Specify whether an agent can be active in a given round and channel.

For deterministic protocols, this is the same as is_agent_active.

Returns:

can_be_active (bool) – Whether the agent can be active in the given round and channel.

can_agent_be_active_any_channel(agent_name: str, round_id: int) → bool[source]#

Specify whether an agent can be active in any channel in a given round.

For non-deterministic protocols, this is true if the agent has some probability of being active.

Returns:

can_be_active (bool) – Whether the agent can be active in the given round.

can_agent_see_channel(agent_name: str, channel_name: str) → bool[source]#

Determine whether an agent can see a channel.

Returns:

can_see_channel (bool) – Whether the agent can see the channel.

get_active_agents_mask_from_rounds_and_seed(round_id: Int[Tensor, '...'], seed: Int[Tensor, '...'] | None) → Bool[Tensor, '... agent channel'][source]#

Get a boolean mask of active agents for a batch of rounds.

Given a batch or rounds, returns a boolean mask indicating which agents are active in each round and channel.

Parameters:

  • round_id (Int[Tensor, "..."]) – The round of the protocol.

  • seed (Int[Tensor, "..."] | None) – The per-environment seed. This is ignored for deterministic protocols, so it can be None.

Returns:

active_agents (Bool[Tensor, “… agent channel”]) – The boolean mask. active_agents[*batch, agent, channel] is True if the agent sends a message in the channel in round round[*batch].

get_agent_visible_channels(agent_name: str) → list[str][source]#

Get the names of the channels visible to an agent.

Parameters:

agent_name (str) – The name of the agent.

Returns:

visible_channels (list[str]) – The names of the channels visible to the agent.

get_verifier_guess_mask_from_rounds_and_seed(round_id: Int[Tensor, '...'], seed: Int[Tensor, '...'] | None) → Bool[Tensor, '...'][source]#

Get a boolean mask indicating when the verifier can make a guess.

Takes as input a tensor of rounds and returns a boolean mask indicating when the verifier can make a guess for each element in the batch.

Parameters:

  • round_id (Int[Tensor, "..."]) – The batch of rounds.

  • seed (Int[Tensor, "..."] | None) – The per-environment seed. This is ignored for deterministic protocols, so it can be None.

Returns:

verifier_turn (Bool[Tensor, “…”]) – Which batch items the verifier can make a guess in.

is_agent_active(agent_name: str, round_id: int, channel_name: str)[source]#

Specify whether an agent is active in a given round and channel.

max_reward(agent_name: str) → float[source]#

Get the maximum possible reward for an agent.

For the verifier, this is the maximum reward it gets for guessing plus the bonus for not guessing in each round (if positive).

For the prover, this is the reward it gets for being accepted by the verifier.

Parameters:

agent_name (str) – The name of the agent to get the maximum reward for.

Returns:

max_reward (float) – The maximum possible reward for the agent.

min_reward(agent_name: str) → float[source]#

Get the minimum possible reward for an agent.

For the verifier, this is the minimum reward it gets for guessing plus the bonus for not guessing in each round (if negative).

For the prover, this 0.

Parameters:

agent_name (str) – The name of the agent to get the maximum reward for.

Returns:

min_reward (float) – The minimum possible reward for the agent.

reward_mid_point_estimate(agent_name: str) → float[source]#

Get an estimate of the expected reward if all agents play randomly.

This is used to compute the mid-point of the reward range for the agent.

For example, if the agent gets reward -1 for a wrong guess and 1 for a correct guess, the mid-point estimate could be 0.

Parameters:

agent_name (str) – The name of the agent to get the reward mid-point for.

Returns:

reward_mid_point (float) – The expected reward for the agent if all agents play randomly.

step_interaction_protocol(env_td: TensorDictBase | NestedArrayDict) → tuple[Bool[Tensor, '...'], Bool[Tensor, '... agent'], Bool[Tensor, '...'], Float[Tensor, '... agent']][source]#

Take a step in the interaction protocol.

Computes the done signals and reward.

Used in the _step method of the environment.

Parameters:

env_td (TensorDictBase | NestedArrayDict) –

The current observation and state. If a NestedArrayDict, it is converted to a TensorDictBase. Has keys:

  • ”y” (… 1): The target value.

  • ”round” (…): The current round.

  • (“agents”, “decision”) (… agent): The decision of each agent.

  • (“agents”, “continuous_decision”) (… agent): (Optional) A more fine-grained version of the decision, which is a float between -1 and 1.

  • (“agents”, “valid_response”) (… agent): (Optional) A boolean mask indicating whether the agent’s response is valid.

  • ”done” (…): A boolean mask indicating whether the episode is done.

  • (“agents”, “done”) (… agent): A boolean mask indicating whether each

    agent is done.

  • ”terminated” (…): A boolean mask indicating whether the episode has been

    terminated.

Returns:

  • shared_done (Bool[Tensor, “…”]) – A boolean mask indicating whether the episode is done because all relevant agents have made a decision.

  • agent_done (Bool[Tensor, “… agent”]) – A boolean mask indicating whether each agent is done, because they have made a decision. This is the same as shared_done for agents which don’t make decisions.

  • terminated (Bool[Tensor, “…”]) – A boolean mask indicating whether the episode has been terminated because the max number of rounds has been reached and the verifier has not guessed.

  • reward (Float[Tensor, “… agent”]) – The reward for the agents.