Reinforcement Learning

Training agents to make decisions through interaction and reward signals.

Foundations
Markov decision processes, value functions, and core RL concepts.
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Policy Methods
Policy gradients, actor-critic, PPO, and direct policy optimization.
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Deep RL
DQN, A3C, and combining deep learning with reinforcement learning.
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Multi-Agent Systems
Cooperative and competitive multi-agent reinforcement learning.
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Model-Based RL
World models, planning, and learning environment dynamics.
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Inverse RL
Learning reward functions from expert demonstrations and behavior.
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Hierarchical RL
Options framework, goal-conditioned policies, and temporal abstraction.
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Offline RL
Learning policies from fixed datasets without environment interaction.
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Safe RL
Constrained optimization, risk-sensitive policies, and safety guarantees.
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