7 docs tagged with "reinforcement-learning"

Combining value-based and policy-based methods for stable and efficient reinforcement learning.

Master the cutting edge with projects in LLM Agents, Generative Adversarial Networks (GANs), and Reinforcement Learning.

Scaling Reinforcement Learning with Deep Learning using Experience Replay and Target Networks.

Optimizing the policy directly: understanding the REINFORCE algorithm, stochastic policies, and the Policy Gradient Theorem.

Mastering the Bellman Equation, Temporal Difference learning, and the Exploration-Exploitation trade-off.

Understanding the Agent-Environment loop, reward signals, and how AI learns to make optimal decisions in dynamic systems.

Exploring the various types of AI agents from Simple Reflex to Learning and Multi-Agent Systems.