from .. import Agent
'''
Implementation of a randomized algorithm which employs a policy which samples uniformly at random from the action space
'''
[docs]class randomAgent(Agent):
"""Randomized RL Algorithm
Implements the randomized RL algorithm - selection an action uniformly at random from the action space. In particular,
the algorithm stores an internal copy of the environment's action space and samples uniformly at random from it.
"""
[docs] def __init__(self):
pass
def reset(self):
pass
[docs] def update_config(self, env, config = None):
"""Updates configuration file for the agent
Updates the stored environment to sample uniformly from.
Args:
env: an openAI gym environment
config: an (optional) dictionary containing parameters for the environment
"""
self.environment = env
pass
[docs] def update_obs(self, obs, action, reward, newObs, timestep, info):
pass
[docs] def update_policy(self, h):
pass
[docs] def pick_action(self, obs, h):
"""Selects an action for the algorithm.
Args:
obs: a state for the environment
h: timestep
Returns:
An action sampled uniformly at random from the environment's action space.
"""
return self.environment.action_space.sample()