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Reinforcement Learning with PyTorch
Overview
Skills Needed
Learn to build and train reinforcement learning agents using PyTorch. Explore techniques for Q-learning, policy gradients, deep Q-networks, and building autonomous agents with PyTorch.
Basic knowledge of PyTorch fundamentals
Understanding of reinforcement learning concepts
Outline
Introduction to Reinforcement Learning
Markov Decision Processes (MDPs)
Q-learning and Value Iteration
Policy Iteration and Policy Gradient Methods
Deep Q-Networks (DQN)
Actor-Critic Methods
Proximal Policy Optimization (PPO)
Deep Deterministic Policy Gradients (DDPG)
Multi-agent Reinforcement Learning
Case Studies in Reinforcement Learning with PyTorch
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