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- Distributed Optimization Description: Distributed optimization is an optimization approach that distributes computation across multiple nodes to improve efficiency and(...) Read more
- Decentralized Data Storage Description: Decentralized data storage is an innovative approach that allows for the distribution of data across multiple locations, rather(...) Read more
- Dyna Description: Dyna is a model-based reinforcement learning algorithm that combines learning and planning. This innovative approach allows agents(...) Read more
- Deep Q-Network Description: A Deep Q-Network (DQN) is a neural network that approximates the Q-value function in reinforcement learning. This approach combines(...) Read more
- Discount Factor Description: The discount factor is a fundamental parameter in reinforcement learning that determines the importance of future rewards in(...) Read more
- Dual Q-Learning Description: Dual Q-Learning is an extension of Q-learning that maintains two separate Q-value estimates. This technique is used in the field of(...) Read more
- Decaying Epsilon Description: Epsilon decay is a strategy in reinforcement learning used to manage an agent's exploration rate over time. In the context of(...) Read more
- Delayed Reward Description: A delayed reward is a reward that is received after a series of actions, rather than immediately. This concept is fundamental in(...) Read more
- Deep Deterministic Policy Gradient Description: The Deep Deterministic Policy Gradient (DDPG) is a reinforcement learning algorithm that combines deep learning techniques with(...) Read more
- Deterministic Policy Gradient Description: The Deterministic Policy Gradient (DPG) is an algorithm that optimizes policies in a deterministic manner, used in the field of(...) Read more
- Dynamic Action Selection Description: Dynamic action selection refers to the process of choosing actions based on the current state and learned policies. This concept is(...) Read more
- Data Efficiency Description: Data efficiency refers to the ability of a reinforcement learning algorithm to learn effectively from limited data. This concept is(...) Read more
- Deterministic Environment Description: A deterministic environment is one where the next state is completely determined by the current state and the action taken. In this(...) Read more
- Dynamic Model Description: A dynamic model in reinforcement learning is an approach used to predict the next state and the reward that will be obtained by(...) Read more
- Dynamic Programming Approach Description: The dynamic programming approach in reinforcement learning involves solving problems by breaking them down into simpler(...) Read more
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