Artificial Intelligence 2.0: The smartest combination of Double Deep Q-Learning, Policy Gradient, Actor Critic, DDPG
InstructorHadelin de Ponteves
Free
FreeThe course provides a highly granular, 20-step approach to building the TD3 model from scratch.
Editorial course preview
This view highlights a concrete, legible example from the course presentation.
The image shows a presenter next to a screen displaying a Jupyter Notebook with Python code for reinforcement learning, training metrics, and a visual simulation of an ant agent.
Dedicated sections cover Artificial Neural Networks, Q-Learning, and Deep Q-Learning to support the main implementation.
One signal from early 2024 notes difficulties understanding the instructor's accent and reading certain mathematical formulas on video slides; however, the displayed update date does not prove these issues were addressed.
One signal from late 2022 suggests a need for more detail regarding the full training process, such as episode management and timesteps; the displayed update date does not prove this was corrected.
This course is best suited for learners who want a highly structured, code-heavy walkthrough of a specific reinforcement learning model.
Score breakdown
The curriculum covers specific technical implementations and theoretical annexes, though depth signals are limited to the TD3 focus.
The course features a step-by-step implementation of the TD3 model and includes training/inference demonstrations.
Instructional signals are mixed, citing both helpful hints and difficulties with accent or slide legibility. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
One signal suggests the content lacks more recent methods like World Models or Vision-Language-Action models. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The curriculum aligns with the technical requirements of implementing reinforcement learning models.