Intrinsic and extrinsic motivation. We examine their influence on learning and performance in various contexts. One text details numerous classroom strategies designed to enhance intrinsic motivation by increasing student autonomy and providing opportunities for self-evaluation and goal setting. Another focuses on the complexities of intrinsic and extrinsic motivation, particularly regarding rewards and their impact on creativity and achievement, proposing new models that integrate these seemingly opposing forces. A third source investigates data-efficient deep reinforcement learning, using deep dynamics models and curiosity-based intrinsic motivation to improve agent performance in high-dimensional environments. Finally, a fourth text introduces an inverse reinforcement learning approach to learning exploration bonuses from demonstrations, aiming to transfer human-like motivations to artificial agents.

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