Difference between revisions of "Deep Reinforcement Learning (DRL)"
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* [[Deep Q Learning (DQN)]] | * [[Deep Q Learning (DQN)]] | ||
| − | * [http://gym.openai.com/ OpenAI | + | * [http://gym.openai.com/ Gym | OpenAI] |
| + | * [https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287 Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) | Steeve Huang] | ||
| + | * [https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-part-ii-trpo-ppo-87f2c5919bb9 Introduction to Various Reinforcement Learning Algorithms. Part II (TRPO, PPO) | Steeve Huang] | ||
* [http://deeplearning4j.org/deepreinforcementlearning.html Guide] | * [http://deeplearning4j.org/deepreinforcementlearning.html Guide] | ||
https://cdn-images-1.medium.com/max/640/1*NyWUkwz1QhrVJj9ygCQ5nA.png | https://cdn-images-1.medium.com/max/640/1*NyWUkwz1QhrVJj9ygCQ5nA.png | ||
| + | https://cdn-images-1.medium.com/max/800/1*BEby_oK1mU8Wq0HABOqeVQ.png | ||
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. | 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. | ||
Revision as of 19:19, 26 May 2018
- Deep Q Learning (DQN)
- Gym | OpenAI
- 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.
