Reinforcement Learning Papers

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. Here are some notable papers in the field of reinforcement learning.

Key Papers

• Deep Q-Network (DQN): This paper introduces the DQN algorithm, which uses deep neural networks to approximate the Q-function in reinforcement learning. The DQN algorithm has been applied to various domains, including playing video games and robot control. Read more

  

• Asynchronous Advantage Actor-Critic (A3C): The A3C algorithm allows multiple agents to learn concurrently, improving the training process. This paper proposes a framework for asynchronous training of deep reinforcement learning agents. Read more

  

• Proximal Policy Optimization (PPO): PPO is an actor-critic method for deep reinforcement learning that is known for its stability and efficiency. This paper presents the PPO algorithm and its applications. Read more

  

Related Resources

• Reinforcement Learning Tutorials
• Deep Learning for Reinforcement Learning

For further reading on reinforcement learning and related topics, visit the Machine Learning Research section of our website.