# Source code for rl_coach.exploration_policies.greedy

#
# Copyright (c) 2017 Intel Corporation
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
#

from typing import List

import numpy as np

from rl_coach.core_types import ActionType
from rl_coach.exploration_policies.exploration_policy import ExplorationParameters, ExplorationPolicy
from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace

class GreedyParameters(ExplorationParameters):
@property
def path(self):
return 'rl_coach.exploration_policies.greedy:Greedy'

[docs]class Greedy(ExplorationPolicy):
"""
The Greedy exploration policy is intended for both discrete and continuous action spaces.
For discrete action spaces, it always selects the action with the maximum value, as given by the agent.
For continuous action spaces, it always return the exact action, as it was given by the agent.
"""
def __init__(self, action_space: ActionSpace):
"""
:param action_space: the action space used by the environment
"""
super().__init__(action_space)

def get_action(self, action_values: List[ActionType]):
if type(self.action_space) == DiscreteActionSpace:
action = np.argmax(action_values)
one_hot_action_probabilities = np.zeros(len(self.action_space.actions))
one_hot_action_probabilities[action] = 1
return action, one_hot_action_probabilities
if type(self.action_space) == BoxActionSpace:
return action_values

def get_control_param(self):
return 0