Hi Geeks, welcome to Part-3 of our Reinforcement Learning Series. In the last two blogs, we covered some basic concepts in RL and also studied the multi-armed bandit problem and its solution methods…
When the agent interacts with the environment, the sequence of experienced tuples can be highly correlated. The naive Q-Learning algorithm that learns from each of these experience tuples in…
In Q-Learning, we represent the Q-value as a table. However, in many real-world problems, there are enormous state and/or action spaces and tabular representation is insufficient. For instance…
This is a PyTorch implementation/tutorial of Deep Q Networks (DQN) from paper Playing Atari with Deep Reinforcement Learning. This includes dueling network architecture, a prioritized replay buffer and double-Q-network training.