Comparison of Reinforcement Learning Algorithms - Medium DQN can handle high-dimensional state spaces, such as images from Atari games, but it still requires discrete action spaces DQN improves upon Q-learning by using several techniques, such as
Advantage Actor-Critic Algorithm - Quant RL DQNs can handle high-dimensional inputs, such as raw images, making them suitable for complex environments On the other hand, Advantage Actor-Critic algorithms typically require feature engineering or domain knowledge to extract relevant features from the environment
[2407. 14151] A Comparative Study of Deep Reinforcement . . . This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models: Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C), within
Implementing A2C (Advantage Actor-Critic) for Complex . . . However, Actor-criticism methods like Advantage Actor-Critic (A2C) offer a dynamic and robust alternative in scenarios requiring more complex strategies This blog, delves into the building and
The Deep Q-Network (DQN) - Hugging Face Deep RL Course The Deep Q-Network (DQN) This is the architecture of our Deep Q-Learning network: As input, we take a stack of 4 frames passed through the network as a state and output a vector of Q-values for each possible action at that state Then, like with Q-Learning, we just need to use our epsilon-greedy policy to select which action to take