I present 4 publications from my research area: reinforcement learning. Let’s discuss it! — [← Previous review][Next review →] Reinforcement learning is a powerful tool for solving complex control problems, and recent research has made significant progress in applying it to a variety of domains. In this post, we will review four papers that showcase the potential of reinforcement learning in different areas, including…

I present 4 publications from my research area: reinforcement learning. Let’s discuss it! — [← Previous review][Next review →] Paper 1: Mastering Atari Games with Limited Data Ye, W., Liu, S., Kurutach, T., Abbeel, P., & Gao, Y. (2021). Mastering Atari Games with Limited Data. arXiv preprint arXiv:2111.00210. EfficientZero is the name given by the authors to their new reinforcement learning algorithm. What makes it different from the many other state-of-the-art algorithms…

We explain step by step how to create a graph, showing the interquartile range with Manim. — Prerequisites In this tutorial, I use version 0.12.0 of Manim. I have no idea if it works with earlier or later versions. I assume you know the basic use of Manim. If you don’t, take a look at the many tutorials for beginners available. The full code is available in the…

I present 4 publications from my research area. Let’s discuss them! — [← Previous review][Next review →] Paper 1: Sparse Reward Exploration via Novelty Search and Emitters Paolo, G., Coninx, A., Doncieux, S., & Laflaquière, A. (2021). Sparse Reward Exploration via Novelty Search and Emitters. arXiv preprint arXiv:2102.03140. The major trade-off in reinforcement learning is the exploration versus exploitation trade-off. Exploration is necessary to find new rewards, and exploitation to capitalize on…

Every Monday, I present 4 publications from my research area. Let’s discuss them! — [← Previous review][Next review →] Paper 1: Reward is enough Silver, D., Singh, S., Precup, D., & Sutton, R. S. (2021). Reward Is Enough. Artificial Intelligence, 103535. The hypothesis that is formulated in this paper, is that reward maximization in a sufficiently complex environment, is a sufficient condition for the emergence of intelligence. The example…

Every Monday, I present 4 publications from my research area. Let’s discuss them! — [← Previous review][Next review →] Paper 1: Distribution-conditioned reinforcement learning for general-purpose policies Nasiriany, S., Pong, V. H., Nair, A., Khazatsky, A., Berseth, G., & Levine, S. (2021). Disco rl: Distribution-conditioned reinforcement learning for general-purpose policies. arXiv preprint arXiv:2104.11707.

Every Monday, I present 4 publications from my research area. Let’s discuss them! — [← Previous review][Next review →] Paper 1: Value Iteration in Continuous Actions, States and Time Lutter M., Mannor S., Peters J., Fox D., Garg A. (2021). Value Iteration in Continuous Actions, States and Time. arXiv preprint arXiv:2105.04682. Reinforcement learning methods were first tabular: one had to choose an action among a finite number of actions, resulting from an observation from…

Every Monday, I present 4 publications from my research area. Let’s discuss them! — [← Previous review][Next review →] Paper 1: Reinforcement Learning with Random Delays Ramstedt, S., Bouteiller, Y., Beltrame, G., Pal, C., & Binas, J. (2020). Reinforcement Learning with Random Delays. arXiv preprint arXiv:2010.02966. Delays between action and reward are common, and are a central problem in RL. Even in the real world: an action can produce a reward…

Every Monday, I present 4 publications from my research area. Let’s discuss them! — [← Previous review][Next review →] Paper 1: A learning gap between neuroscience and reinforcement learning Wauthier, S. T., Mazzaglia, P., Çatal, O., De Boom, C., Verbelen, T., & Dhoedt, B. (2021). A learning gap between neuroscience and reinforcement learning. arXiv preprint arXiv:2104.10995. Here are two possible configurations. The reward is represented by the red circle. In the first configuration, the…