seldonian.RL.Agents.Policies.Policy.Discrete_Action_Policy

class Discrete_Action_Policy(hyperparam_and_setting_dict, env_description)

Bases: Policy

__init__(hyperparam_and_setting_dict, env_description)

General policy class where actions are discrete. Converts input actions into 0-indexed actions.

Parameters:

  • hyperparameter_and_setting_dict – Specifies the environment, agent, number of episodes per trial, and number of trials

  • env_description (Env_Description) – an object for accessing attributes of the environment

__repr__()

Return repr(self).

Methods

choose_action(obs)

Defines how to select an action given an observation, obs

construct_basis_and_linear_FA(env_description, hyperparam_and_setting_dict)

Create a basis and linear function approximator from an environment description and dictionary specification

Parameters:

  • env_description (Env_Description) – an object for accessing attributes of the environment

  • hyperparameter_and_setting_dict – Specifies the environment, agent, number of episodes per trial, and number of trials

from_0_indexed_action_to_environment_action(action_0_indexed)

Convert 0-indexed action to env-specific action

from_environment_action_to_0_indexed_action(env_action)

Convert env-specific action to 0 indexed action

get_action_values_given_state(obs)

Get all parameter weights possible in a given observation

get_params()

Get the current parameters (weights) of the agent

Returns:

array of weights

get_prob_this_action(obs, action)

Get probability of taking an action given an observation. Does not necessarily need to be overridden, but is often called from self.get_probs_from_observations_and_actions()

get_probs_from_observations_and_actions(observations, actions, behavior_action_probs)

Get probabilities for each observation and action in the input arrays

make_state_action_FA(env_description, hyperparam_and_setting_dict)

Create a function approximator from an environment description and dictionary specification

Parameters:

  • env_description (Env_Description) – an object for accessing attributes of the environment

  • hyperparameter_and_setting_dict – Specifies the environment, agent, number of episodes per trial, and number of trials

Returns:

function approximator, type depends on whether observation space is discrete or continous

set_new_params(new_params)

Set the parameters of the agent

Parameters:

new_params – array of weights