Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves...

Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that...

losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search, Deep Q-networks...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward...

is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when...

worked on robot learning algorithms from deep predictive models. She delivered a massive open online course on deep reinforcement learning. She was the first...

Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations....

Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression...

predictions. A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike...

(Japanese chess) after a few days of play against itself using reinforcement learning. In 2020, DeepMind made significant advances in the problem of protein...

his cutting-edge research in robotics and machine learning, particularly in deep reinforcement learning. In 2021, he joined AIX Ventures as an Investment...

co-authored the paper "Deep Reinforcement Learning from Human Preferences" (2017) and other works developing reinforcement learning from human feedback (RLHF)...

Starting in 2013, significant progress was made following the deep reinforcement learning approach, including the development of programs that can learn...

four of the world's best Gran Turismo drivers using deep reinforcement learning. In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously...

The actor-critic algorithm (AC) is a family of reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods...

research scientist at Google DeepMind and a professor at University College London. He has led research on reinforcement learning with AlphaGo, AlphaZero and...

Jian; Han, Jiawei (2018). Curriculum learning for heterogeneous star network embedding via deep reinforcement learning. pp. 468–476. doi:10.1145/3159652...

explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical...

Alternative to Reinforcement Learning". arXiv:1703.03864 [stat.ML]. Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J (20 April 2018). "Deep Neuroevolution:...

Furthermore, AlphaGo Zero performed better than standard deep reinforcement learning models (such as Deep Q-Network implementations) due to its integration of...

nor the robot. In 2017, OpenAI and DeepMind applied deep learning to the cooperative inverse reinforcement learning in simple domains such as Atari games...

Wierstra, Daan; Riedmiller, Martin (2013). "Playing Atari with Deep Reinforcement Learning". arXiv:1312.5602 [cs.LG]. Mnih, Volodymyr; Kavukcuoglu, Koray;...

resembles Ridge regression. Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned...

made significant advances in deep learning and reinforcement learning, and pioneered the field of deep reinforcement learning which combines these two methods...

peer-to-peer networks, Internet privacy, social networks, and deep reinforcement learning. He is the Dean of Engineering and Computer Science at NYU Shanghai...

classification benchmarks and to policy-gradient-based reinforcement learning. Variational Bayes-Adaptive Deep RL (VariBAD) was introduced in 2019. While MAML...

address fusion heating, measurement, and power production. A deep reinforcement learning system has been used to control a tokamak-based reactor. The...