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Continuous space: action space
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Related lectures (32)
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Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.
Policy Gradient and Actor-Critic Methods: Eligibility Traces Explained
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Approximation Landau: Ising Model
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Covers bounded operators between normed vector spaces, emphasizing the importance of continuity and exploring applications like the Fourier transform.
Reinforcement Learning: Q-Learning
Covers Q-Learning in reinforcement learning, exploring action values, policies, and the societal impact of algorithms.
Reinforcement Learning: Policy Gradient and Actor-Critic Methods
Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.
Elements of Reinforcement Learning
Introduces the fundamental elements of Reinforcement Learning and demonstrates their application with the Acrobot system.
Deep Reinforcement Learning: Proximal Policy Optimization Techniques
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Eligibility Traces for Policy Gradient and Actor-Critic
Explores eligibility traces in policy gradient and actor-critic architectures, leading to an elegant online learning rule.
Policy Gradient Methods: Direct Action Learning in Reinforcement Learning
Covers policy gradient methods, focusing on direct action learning and optimizing rewards in reinforcement learning.
Modeling the input space
Explores modeling continuous input spaces in reinforcement learning using neural networks and radial basis functions.
Landscape and Generalisation in Deep Learning
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Mathematics of Data: Optimization Basics
Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.
Reinforcement Learning: Q-Learning
Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.
Learning to Find a Goal
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Normed Spaces
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Covers normed spaces, dual spaces, Banach spaces, Hilbert spaces, weak and strong convergence, reflexive spaces, and the Hahn-Banach theorem.
Markov Chains: Basics and Applications
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Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.
Weak Derivatives: Definition and Properties
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Covers weak derivatives, their properties, and applications in functional analysis.
Distributional Derivatives
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Explores distributional derivatives, continuity, boundedness of linear operators, and weak-* continuity.
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