prevision_quantum_nn.applications.reinforcement_learning.policy¶
Policy module
contains the classes to handle policies in the Reinforcement Learning framework.
Module Contents¶
Classes¶
Policy, base class for definiing policies. |
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Behavior Policy. |
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Target Policy. |
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prevision_quantum_nn.applications.reinforcement_learning.policy.LEARNING_TYPES= ['monte_carlo', 'q_learning', 'td_learning']¶
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class
prevision_quantum_nn.applications.reinforcement_learning.policy.Policy(params)¶ Policy, base class for definiing policies.
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params¶ parameters of the policy
- Type
dictionary
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iteration¶ the current iteration at which the policy is
- Type
int
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epsilon¶ the parameter of the epsilon-greedy method
- Type
float
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epsilon_decay¶ the decay parameter that will decay epsilon after each iteration
- Type
float
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gamma¶ the parameter of the temporal difference method
- Type
float
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learner¶ the learner used to evaluate the Q-value function
- Type
RLLearner
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use_memory_replay¶ if True, memory replay will be activated
- Type
bool
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memory_replay_period¶ the period of the memory replay method
- Type
int
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learning_type¶ one instance of learning_types. It can be “monte_carlo”, “q_learning” or “td_learning”
- Type
str
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associate_learner(self, learner)¶ associates a learner to the policy
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step(self, state)¶ Step one iteration further.
- Parameters
state (numpy array) – state at which the agent is before taking an action
- Returns
- int
action taken by the policy
- Return type
action
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update_epsilon_greedy_parameter(self)¶ updates epsilon if higher than minimum
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fit_learner(self, state, action, reward, new_state)¶ Fits the learner.
- Parameters
state (numpy array) – state at which the learner is
action (int) – action that has just been taken in that state
reward (float) – reward obtained by taking this action in the current state
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abstract
get_action(self, state, action=None)¶ get action for current state to be overridden
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class
prevision_quantum_nn.applications.reinforcement_learning.policy.BehaviorPolicy(params)¶ Bases:
prevision_quantum_nn.applications.reinforcement_learning.policy.PolicyBehavior Policy.
Implements an exploratory policy with the epsilon greedy method.
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get_action(self, state, action=None)¶ Returns an action to be taken by the agent.
According to epsilon greedy policy.
- Parameters
state (numpy array) – state at which the agent is
action (int) – action that has just been taken
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class
prevision_quantum_nn.applications.reinforcement_learning.policy.TargetPolicy(params)¶ Bases:
prevision_quantum_nn.applications.reinforcement_learning.policy.PolicyTarget Policy.
Implements an purely exploitation policy without random actions
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get_action(self, state, action=None)¶ Get action.
- Parameters
state (numpy array) – state at which the action needs to be taken
- Returns
- int
the action taken at that state
- Return type
action
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