Difference between revisions of "Deep Reinforcement Learning (DRL)"
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==== OTHER: Learning; MDP, Q, and SARSA ==== | ==== OTHER: Learning; MDP, Q, and SARSA ==== | ||
* [[Markov Decision Process (MDP)]] | * [[Markov Decision Process (MDP)]] | ||
| − | * [[Deep Q | + | * [[Deep Q Network (DQN)]] |
* [[Neural Coreference]] | * [[Neural Coreference]] | ||
* [[State-Action-Reward-State-Action (SARSA)]] | * [[State-Action-Reward-State-Action (SARSA)]] | ||
Revision as of 14:14, 1 September 2019
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OTHER: Learning; MDP, Q, and SARSA
- Markov Decision Process (MDP)
- Deep Q Network (DQN)
- Neural Coreference
- State-Action-Reward-State-Action (SARSA)
OTHER: Policy Gradient Methods
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- Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) | Steeve Huang
- Introduction to Various Reinforcement Learning Algorithms. Part II (TRPO, PPO) | Steeve Huang
- Guide
Goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce. Like humans, reinforcement learning algorithms sometimes have to wait a while to see the fruit of their decisions. They operate in a delayed return environment, where it can be difficult to understand which action leads to which outcome over many time steps.
