Deep Reinforcement Learning Through Policy Optimization
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Deep Reinforcement Learning Through Policy Optimization
Reinforcement Learning (Deep RL) has seen several breakthroughs in recent years. In this tutorial we will focus on recent advances in Deep RL through policy gradient methods and actor critic methods. These methods have shown significant success in a wide range of domains, including continuous-action domains such as manipulation, locomotion, and ...
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Machinoai on Instagram: "Proximal Policy Optimisation (PPO) in RLHF: PPO is an algorithm that widely used in reinforcement learning from human feedback, where the base model learns to optimise the response according to the user preferences and controls the maximum shift in the base model. For an example , You ask your chef to prepare a cup of coffee but the he is unaware of your preferences but later if you give your feedback , he can customise the ingredients accordingly. The base model has bee
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