Systems and methods for deep reinforcement learning using a brain-artificial intelligence interface

A computer system for utilizing reinforcement signals of a user or an environment through at least one sensor to alter a behavior of an artificial intelligence agent, comprising:

• a processor;
• and
• a memory, operatively coupled to the processor and storing instructions that, when executed by the processor, cause the computer system to:
• analyze the environment including objects, events, or actions therein;
• collect sensory information correlated to a state of the user or the environment from the at least one sensor, wherein the sensory information includes physiological signals of preferences of the user;
• identify, via the processor, one or more reinforcement signals of the user or the environment based on the collected sensory information;
• alter the artificial intelligence agent to respond to the one or more reinforcement signals;
• and
• alter the environment based on the identification of the one or more reinforcement signals.

A system for detecting reinforcement signals of a user in one or more objects, events, or actions within an environment through at least one sensor, comprising:

• a machine learning module operatively connected to the at least one sensor and configured to process sensory information for the user from the at least one sensor in response to the environment, wherein the machine learning module includes:
• a processing circuit;
• a hybrid human brain computer interface (hBCI) module, comprising:
• one or more sensory input devices;
• a machine learning module for processing input signals from the one or more sensory input devices;
• and
• an environment module for generating a virtual environment;
• and
• a reinforcement learning module;
• and
• a controller operatively connected to the machine learning module.