Reinforcement vs Motivated Learning

Motivated learning (ML) is a method of learning in embodied agents driven by pain-based motivation, goal creation, and learning.

Abstract goals are created in ML systems to reduce abstract pains and satisfy primitive goals like survival or well-being.

Reinforcement Learning (RL) uses a single value function to maximize rewards based on measurable, predictable objectives set by the designer.

RL actions depend on the state of the environment and attempt to maximize rewards.

Motivated Learning, unlike RL, uses multiple value functions, one for each goal, to solve a minimax problem and achieve internal rewards.

Motivated Learning tackles unpredictable objectives with internal rewards, while RL relies on predefined rewards.

In Motivated Learning, actions are determined by both the state of the environment and the agent's current state, allowing for more flexible behavior.

Consciousness is an attention-driven cognitive process that encompasses perception, feelings, emotions, motivations, thoughts, plans, and action monitoring.

A machine is considered conscious if, in addition to feeling, perceiving, acting, learning, and remembering, it possesses a working memory mechanism to focus attention, plan, and evaluate actions.

Attention is a selective cognitive process that filters and prioritizes information for perception, action, and other cognitive experiences.

Motivated Learning (ML) is a learning paradigm focused on embodied agents. It emphasizes self-motivated goal setting and learning based on pain avoidance.

In Motivated Learning, goals emerge from a need to alleviate abstract pains, which can be conceptualized as internal pressures or unmet desires.

Unlike Reinforcement Learning, which relies on predefined reward functions, Motivated Learning uses internal rewards based on goal achievement and pain avoidance.

Motivated Learning's approach to rewards is more flexible and adaptable, allowing agents to learn in environments where external rewards are not readily available.

The 'minimax problem' in Motivated Learning refers to the agent's strategy of minimizing pain and maximizing goal attainment. This involves balancing multiple objectives, including pain avoidance and goal achievement.

Motivated Learning, with its ability to set internal goals and adapt to changing circumstances, is considered more stable and effective in complex environments compared to Reinforcement Learning.

The working memory mechanism in a conscious machine acts as a central executive that uses attention to focus on specific information, plan future actions, and evaluate the effectiveness of those actions.

Consciousness in machines can be realized through integration of multiple cognitive processes, including perception, action, learning, and internal motivation, all coordinated by a central executive function.