Deep Reinforcement Learning

1. Value-Based Deep RL

2. Policy Gradients and PPO

Policies as Trainable ObjectsREINFORCE IntuitionActor-Critic MethodsTRPOPPOSACChapter Debrief

3. Safety, Generalization, and Exploration

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Deep Reinforcement Learning>Policy Gradients and PPO

Chapter Debrief

REINFORCE → raw policy gradient learning

A2C → use a critic to reduce variance

GAE → smooth feedback over time

TRPO → trust regions for safe updates

PPO → clip ratios for simplicity and stability

RLHF → human feedback as reward model in a PPO loop

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