reinforcement learning
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What is reinforcement learning?
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions in an environment to maximize a reward. The agent takes actions, observes the environment's state, and receives a reward or penalty. Through trial and error, the agent learns an optimal policy, which maps states to the best actions to take. It is commonly used in robotics, game playing (like AlphaGo), and autonomous driving, and recommender systems.
What other technologies are related to reinforcement learning?
reinforcement learning Competitor Technologies
imitation learning
Imitation learning, including behavioral cloning and inverse reinforcement learning, offers alternative approaches to learning optimal policies, often requiring less interaction with the environment than reinforcement learning.
Bandits
No summary available
Bandit algorithms, such as multi-armed bandits, offer simpler and more efficient solutions for exploration-exploitation problems in certain scenarios, providing an alternative to full reinforcement learning.
multi-armed bandits
No summary available
See explanation for Bandits. Bandit algorithms, such as multi-armed bandits, offer simpler and more efficient solutions for exploration-exploitation problems in certain scenarios, providing an alternative to full reinforcement learning.
Bayesian optimization
Bayesian optimization is useful when the RL agent is interacting with a black-box function or environment that is costly or difficult to evaluate and serves as a competitor in finding the best parameters.
Number of organizations that mention technology
2,379
reinforcement learning
89
imitation learning
15
Bandits
32
multi-armed bandits
134
Bayesian optimization
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reinforcement learning Complementary Technologies
deep learning
Deep learning provides powerful function approximation capabilities, which are essential for many reinforcement learning algorithms, especially in high-dimensional state spaces.
Natural Language Processing
NLP can be used in reinforcement learning when the environment provides textual feedback or instructions, allowing the agent to understand and respond appropriately.
transfer learning
Transfer learning can significantly improve the sample efficiency of reinforcement learning by leveraging knowledge gained from previous tasks or environments.
Number of organizations that mention technology
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Which job functions commonly mention reinforcement learning?
Applied Scientist
3.2% of all Applied Scientist jobs mention reinforcement learning
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jobs on Sumble
Machine Learning
1.2% of all Machine Learning jobs mention reinforcement learning
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jobs on Sumble
AI Engineer
1.1% of all AI Engineer jobs mention reinforcement learning
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jobs on Sumble
Data Scientist
0.7% of all Data Scientist jobs mention reinforcement learning
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jobs on Sumble
Data, Analytics & Machine Learning
0.2% of all Data, Analytics & Machine Learning jobs mention reinforcement learning
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jobs on Sumble
Which organizations are mentioning reinforcement learning?
Amazon
Retail Trade
Microsoft
Scientific and Technical Services
JPMorgan Chase
Finance and Insurance
Apple
Scientific and Technical Services
TU Delft
Educational Services
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