Titlebook: Deep Reinforcement Learning with Python; RLHF for Chatbots an Nimish Sanghi Book 2024Latest edition Nimish Sanghi 2024 Artificial Intellige

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The Foundation: Markov Decision Processes,s under the branch of probability that models sequential decision-making behavior. Although most of the problems you‘ll study in reinforcement learning are modeled as . (MDP), this chapter starts by introducing Markov chains (MC) followed by Markov reward processes (MRP). Next, the chapter discusses

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Model-Based Approaches,nt transitions from one state to another. Equations . and . clearly indicate that .(.) and .(., .) depend on two components, the transition dynamics and the next state/state-action values. To lay the foundations of RL learning, this chapter starts with the simplest setup—one in which the transition

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Improvements to DQN**, NoisyNets DQN, C-51 (Categorical 51-Atom DQN), Quantile Regression DQN, and Hindsight Experience Replay. All the examples in this chapter are coded using PyTorch. This is an optional chapter with each variant of DQN as a standalone topic. You can skip this chapter in the first pass and come back to

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Combining Policy Gradient and Q-Learning,s. You looked at policy gradients in Chapter .. Neural network training requires multiple iterations, and Q-learning, an off-policy approach, enables you to reuse sample transitions multiple times, giving you sample efficiency. However, Q-learning can be unstable at times. Further, it is an indirect

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