Collaborating Authors

Learning in PyTorch Modern Reinforcement Learning: Deep Q


You will then learn how to implement these in pythonic and concise PyTorch code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist. You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras.


AITopics Original Links

Every year there is a brand new reinforcement learning competition. This usually consists of new organizers, and a new website! Instead of replacing the old website every year and breaking hundreds of links, we use a different subdomain each year. So, this page will always exist at: And the specific websites for different years are: NIPS Reinforcement Learning Workshop: Benchmarks and Bakeoffs NIPS Reinforcement Learning Workshop: Benchmarks and Bakeoffs II ICML Reinforcement Learning and Benchmarking Event NIPS Workshop: The First Annual Reinforcement Learning Competition The 2008 Reinforcement Learning Competition::

The Reinforcement Learning Competition 2014

AI Magazine

Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

Calibrated Model-Based Deep Reinforcement Learning Machine Learning

Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from modern deep learning systems---can be inaccurate and impose a bottleneck on performance. This paper explores which uncertainties are needed for model-based reinforcement learning and argues that good uncertainties must be calibrated, i.e. their probabilities should match empirical frequencies of predicted events. We describe a simple way to augment any model-based reinforcement learning agent with a calibrated model and show that doing so consistently improves planning, sample complexity, and exploration. On the \textsc{HalfCheetah} MuJoCo task, our system achieves state-of-the-art performance using 50\% fewer samples than the current leading approach. Our findings suggest that calibration can improve the performance of model-based reinforcement learning with minimal computational and implementation overhead.

Learning to Learn More: Meta Reinforcement Learning


The ELI5 definition for Reinforcement Learning would be training a model to perform better by iteratively learning from its previous mistakes. Reinforcement learning provides a framework for agents to solve problems in case of real-world scenarios. They are able to learn rules (or policies) to solve specific problems, but one of the major limitations of these agents are that they are unable to generalize the learned policy to newer problems. A previously learned rule would cater to a specific problem only, and would often be useless for other (even similar) cases. A good meta-learning model on the other hand, is expected to generalize to new tasks or environments that have not been encountered by the model in training.