Build an intelligent Agent using DDPG, an advanced deep-learning RL algorithm. — Prerequisites Essential elements of Reinforcement Learning Reinforcement Learning: Temporal Difference Learning Reinforcement Learning: Q-Learning Deep Q Learning: A Deep Reinforcement Learning Algorithm Unlocking the secrets of Actor-Critic Reinforcement Learning- A beginners guide Unlock the secrets of DDPG in Reinforcement Learning

A simple step-by-step explanation of Deep Deterministic Policy Gradient(DDPG) in RL — How would you train a robotic arm to grasp objects or for locomotion which is a continuous control problem with continuous states and continuous actions? What is a continuous control problem in RL? A continuous control problem in RL is where an agent needs to take actions from a continuous action space is much more complex than the…

Master the implementation of the popular Deep Reinforcement Learning algorithm Proximal Policy Optimization (PPO) using PyTorch. — Proximal Policy Optimization is the Reinforcement algorithm that is easy to implement, handles continuous states and actions, has reduced variance, and is sample-efficient. PPO belongs to the policy gradient methods for reinforcement learning. It alternates between Sampling data through interaction with the environment and

Discover the Versatile Deep Reinforcement Learning Algorithm Used in ChatGPT’s RL Capabilities — Proximal Policy Optimization (PPO) — ChatGPT is currently the most popular Large Language model, significantly impacting natural language processing and disrupting the world. It is trained on large and diverse data sources, such as news articles, books, websites, and social media posts, and uses PPO Reinforcement Learning involving Human Feedback.

Understanding Actor-Critic Mechanisms, Different Flavors of Actor-Critic Algorithms, and a Simple Implementation in PyTorch — Concepts you should Know: Reinforcement Learning: Temporal Difference Learning Reinforcement Learning: Q-Learning Deep Q Learning: A Deep Reinforcement Learning Algorithm An Intuitive Explanation of Policy Gradient What is the Actor-Critic algorithm? Actor-Critic is a Reinforcement Learning algorithm that optimizes the agent’s actions based on the environment's feedback.

A Simple Explanation of Policy Gradient for Reinforcement Learning with very little Math — Your goal is to teach a simulated robot to move forward using Reinforcement Learning(RL). A simulated robot must explore the unknown environment with an infinitely large number of actions it can take to find which actions yield the best results to keep moving forward.

Train an agent to explore an environment using Reinforcement learning and then use the optimized policy generated to perform the task like a pro. — If you’re wondering how to apply reinforcement learning (RL) to play a game like a champ, read on… To accomplish the goal, you need to train an RL agent that uses trial and error to explore the game(environment) by interacting with the game and finding what actions in different states…

An easy-to-understand explanation of Deep Q-Learning with PyTorch code implementation — Deep Reinforcement Learning Deep Reinforcement Learning combines Reinforcement Learning algorithms with Artificial Neural Networks. It allows the agent to learn an optimal policy for sequential decision problems by maximizing the cumulative future reward. Deep RL = RL Algorithm + Artificial Neural Network Reinforcement Learning is a goal-oriented algorithm where the agent aims to…

The Art of Note-Taking for Capturing and Retaining Information for Effective Learning — There are several techniques for taking notes, and the best method depends on the types of notes you want to take. Notes are taken for a variety of purposes : Learning a new topic: Notes can be a helpful tool for learning and understanding new information. Long-term retention: Reviewing your…