Abstract: Proximal policy optimization (PPO) is the state-of the-art most effective model-free reinforcement learning algorithm. Its powerful policy search ability ...

There are many different types of reinforcement learning algorithms, but two main categories are “model-based” and “model-free” RL. They are both inspired by our understanding of learning ...

The reinforcement learning can qualify as the model-based if the machine learning algorithms ... model-free reinforcement learning does not need a model for the environment. It helps to learn a value ...

Model-free RL agents use value functions or policy ... Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto: a classic textbook that covers the fundamentals and ...

Abstract: Most reinforcement learning ... and obtaining precise models for dynamic environments in real-time is challenging. To address these issues, this paper proposes a model-free RL algorithm ...

An agent can be called the unit cell of reinforcement learning. An agent receives rewards from the environment. It is optimised through algorithms ... that use models and planning are called ...

Reinforcement learning (RL ... A research team from Meta FAIR introduced MR.Q, a model-free RL algorithm incorporating model-based representations to improve learning efficiency and generalization.

PyTorch implementation of the MR.Q algorithm from Towards General-Purpose Model-Free Reinforcement Learning by Scott Fujimoto, Pierluca D'Oro, Amy Zhang, Yuandong Tian, and Michael Rabbat. Benchmark ...

Experience replay is widely used in AI to bootstrap reinforcement learning (RL) by enabling an agent to remember ... exploitation trade-off parameter β given in Table 3. The model-free algorithm does ...