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- State Representation Description: State representation in reinforcement learning refers to how the current state of the environment is encoded for the agent. This(...) Read more
- Self-Play Description: Self-Play is a training method in the field of reinforcement learning where an agent interacts with itself to improve its(...) Read more
- Stochastic Policy Description: A stochastic policy in the context of reinforcement learning is an approach that defines a probability distribution over the(...) Read more
- Skill Acquisition Description: Skill acquisition in the context of reinforcement learning refers to the process by which an agent, which can be an algorithm or an(...) Read more
- State Space Description: The 'State Space' refers to the set of all possible states that an agent can occupy in a given environment. In the context of(...) Read more
- Smoothing Algorithm Description: The smoothing algorithm is a technique used in the field of reinforcement learning to reduce fluctuations in the reward signals(...) Read more
- State-Dependent Exploration Description: State-Dependent Exploration is a strategy used in reinforcement learning that adjusts an agent's exploration rate based on its(...) Read more
- Subgoal Description: A subgoal in the context of reinforcement learning refers to an intermediate goal that an agent must achieve to facilitate the(...) Read more
- Stochastic Gradient Description: Stochastic gradient is an optimization method used in the field of machine learning. This approach is based on the idea that(...) Read more
- State-Action Value Function Description: The State-Action Value Function (Q) is a fundamental concept in reinforcement learning, referring to a function that estimates the(...) Read more
- Satisfaction Rate Description: The Satisfaction Rate is a metric that measures the proportion of successful actions taken by an agent in a given environment. In(...) Read more
- StyleGAN Description: StyleGAN is a type of Generative Adversarial Network (GAN) that stands out for its ability to generate high-quality and realistic(...) Read more
- Supervised GAN Description: A Supervised GAN is a variant of Generative Adversarial Networks (GANs) that incorporates supervised learning techniques to enhance(...) Read more
- Semi-supervised GAN Description: Semi-supervised GANs are a variant of Generative Adversarial Networks (GANs) that combine labeled and unlabeled data to train deep(...) Read more
- Self-attention GAN Description: Self-attention GANs are a variant of Generative Adversarial Networks (GANs) that incorporate self-attention mechanisms to enhance(...) Read more
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