METHODS AND APPARATUS FOR IMPLEMENTING REINFORCEMENT LEARNING

Methods and apparatus for implementing reinforcement learning (RL) are provided. A method of operation for a node implementing RL, wherein the node instructs actions in an environment in accordance with a policy generated by a RL agent, wherein the RL agent models the environment and encodes a state of the environment using a set of features, comprises obtaining an intent, wherein the intent specifies one or more criteria to be satisfied by the environment. The method further comprises determining a Companion Markov Decision Process (CMDP) that encodes states of the environment using a subset of the set of features used by the RL agent. The method further comprises generating a finite state automaton that represents the intent as a series of logic states, and computing a product of CMDP output states and logic states, wherein the product contains all of the potential combinations of a CMDP output state and a logic state. The method further comprises selecting an action to be performed on the environment from one or more suggested actions obtained from the policy, the selection being based on the product of CMDP output states and logic state.