Reinforcement Learning Research

Reinforcement learning enables AI agents to learn optimal behaviors through interaction with environments, receiving rewards or penalties for their actions.

England, particularly through DeepMind, has been a global leader in reinforcement learning research, achieving groundbreaking results in game playing and other applications.

Combining Q-learning with deep neural networks, enabling RL in high-di

Policy Gradient Methods

Directly optimizing the policy by estimating gradients and updating policy parameters.

live demo · related simulation● LIVE

DeepMind in London has been a global leader in reinforcement learning,

English universities conduct research on reinforcement learning theory, algorithms, and applications.

RL agents achieve superhuman performance in games from Go to chess to video games.

Frequently asked questions

What is the balance between exploring new actions and exploiting known good actions in reinforcement learning?

Balancing exploring new actions with exploiting known good actions.

How can we ensure RL agents behave safely, especially in real-world applications?

Ensuring RL agents behave safely, especially in real-world applications.

Why do RL agents often struggle to generalize to new environments or tasks?

RL agents often struggle to generalize to new environments or tasks.

What are more sample-efficient algorithms for reinforcement learning?

More sample-efficient algorithms

▶ Try it live

Everything above runs in your browser — open Decision Tree Live and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Decision Tree Live simulation

Reinforcement Learning Research - Agents, Games & Robotics