An electronic device for signal processing with embedded optimization of electrical energy consumption, comprising: - at least one sensor (22, 24, 26, 28, 30, 32) designed to measure, at each of a plurality of successive times, a state value of the electronic device, - at least one actuator (34, 36, 38) designed to adjust, at each of the plurality of successive times, an action value on the electronic device, - hardware for signal and data storage, comprising at least one correspondence table that, for each pair formed by one of several possible predetermined values (s 1,..., s m) of the measurable state and one of several possible predetermined values (a 1,..., a n) of the adjustable action, assigns at least one value representing an optimization of the electrical energy consumption, referred to as a quality value, - signal processing hardware, comprising an electrical energy consumption manager with a reinforcement learning engine designed, at each of the plurality of successive times, on the basis of the measured state, to: • update at least one quality value in each correspondence table, on the basis of said measured state and the action of the preceding time that led to said measured state, • selectively choose between an exploitation of updated quality values and a random exploration in each correspondence table, the random exploration being defined as the random selection of an action value in each correspondence table, according to a statistical parameter expressing a rate of random explorations to be performed in the plurality of successive times and a rate complementary to 1 of exploitations to be performed in the same plurality of successive times, • select a new action on the basis of a result of the exploitation or the exploration, and • transmit the value of said new action to said at least one actuator (34, 36, 38) for adjustment, characterized in that the reinforcement learning engine has a parameter (Δ) for limiting the random exploration which applies during its execution if it is selected, i.e. before the selection of a new action as a function of the result of the exploitation or exploration, comprising an exploration distance value in each correspondence table, around a pre-selectable action value in each correspondence table, beyond which an action value cannot be selected by random exploration. - at least one sensor (22, 24, 26, 28, 30, 32) designed to measure, at each of a plurality of successive times, a state value of the electronic device, - at least one actuator (34, 36, 38) designed to adjust, at each of the plurality of successive times, an action value on the electronic device, - hardware for signal and data storage, comprising at least one correspondence table that, for each pair formed by one of several possible predetermined values (s 1,..., s m) of the measurable state and one of several possible predetermined values (a 1,..., a n) of the adjustable action, assigns at least one value representing an optimization of the electrical energy consumption, referred to as a quality value, - signal processing hardware, comprising an electrical energy consumption manager with a reinforcement learning engine designed, at each of the plurality of successive times, on the basis of the measured state, to: • update at least one quality value in each correspondence table, on the basis of said measured state and the action of the preceding time that led to said measured state, • selectively choose between an exploitation of updated quality values and a random exploration in each correspondence table, the random exploration being defined as the random selection of an action value in each correspondence table, according to a statistical parameter expressing a rate of random explorations to be performed in the plurality of successive times and a rate complementary to 1 of exploitations to be performed in the same plurality of successive times, • select a new action on the basis of a result of the exploitation or the exploration, and • transmit the value of said new action to said at least one actuator (34, 36, 38) for adjustment, • update at least one quality value in each correspondence table, on the basis of said measured state and the action of the preceding time that led to said measured state, • selectively choose between an exploitation of updated quality values and a random exploration in each correspondence table, the random exploration being defined as the random selection of an action value in each correspondence table, according to a statistical parameter expressing a rate of random explorations to be performed in the plurality of successive times and a rate complementary to 1 of exploitations to be performed in the same plurality of successive times, • select a new action on the basis of a result of the exploitation or the exploration, and • transmit the value of said new action to said at least one actuator (34, 36, 38) for adjustment,