# Hierarchical Reinforcement Learning (HRL)

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- The Promise of Hierarchical Reinforcement Learning | Yannis Flet-Berliac - The Gradient
- Hierarchical Reinforcement Learning | David Jardim
- Reinforcement Learning (RL):
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- Q Learning
- State-Action-Reward-State-Action (SARSA)
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Deep Q Network (DQN)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional Reinforcement Learning (RL) methods to solve more complex tasks.

## HIerarchical Reinforcement learning with Off-policy correction (HIRO)

- Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science
- Data-Efficient Hierarchical Reinforcement Learning | O. Nachum, S. Gu, H. Lee, and S. Levine - Google Brain

HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods.