Subscribe to RL Weekly

Get the highlights of reinforcement learning in both research and industry every week.


Thank you for reading RL Weekly. I created a Google Form to make it easier to leave feedback. Please feel free to leave any feedback!

Next week, I will be flying back to Princeton to finish my bachelor’s degree, so issue #31 could be delayed. I hope to bring you more exciting news in reinforcement learning in the future!

- Ryan

Dynamics-aware Embeddings

William Whitney13, Rajat Agarwal1, Kyunghyun Cho13, Abhinav Gupta23

1Department of Computer Science, New York University, 2Robotics Institute, Carnegie Mellon University, 3Facebook AI Research

What it says

“The intuition is simple: preserve as much information as possible about the *outcomes* of a state or action while minimizing its description length. This leads to embeddings with smooth structure that make it easy to do RL.” - William Whitney, the first author.

Most popular RL algorithms are trained end-to-end: the convolutional layers that extract features are trained with the fully connected layers that choose action or estimate values. Instead of this end-to-end approach, the authors propose a new representation learning objective DynE (Dynamics-Aware Embedding). DynE learns a state encoder and an action encoder by maximizing a variational lower bound (Section 2.2), then learns an action decoder that produces low-level action sequence from a high-level action (Section 3.1). With a simple modification (Section 3.2), DynE can be used with TD3 (Twin-delayed DDPG).

DynE-TD3 is tested with Reacher and 7DoF tasks from MuJoCo. DynE-TD3 learns faster that other baselines (PPO, TD3, SAC, and SAC-LSP) and its action representations are transferable to similar environments (Page 8, Figure 4). The authors also compare using just the state embedding (S-Dyne-TD3) and using both embeddings (SA-Dyne-TD3). In simpler environments, both achieve similar performance, but SA-Dyne-TD3 shines in harder environment. Both perform better than TD3 and VAE-TD3 (Page 9, Figure 5).

Read more

External resources

OpenSpiel: A Framework for Reinforcement Learning in Games

Marc Lanctot1*, Edward Lockhart1*, Jean-Baptiste Lespiau1*, Vinicius Zambaldi1*, Satyaki Upadhyay2, Julien Pérolat1, Sriram Srinivasan2, Finbarr Timbers1, Karl Tuyls1, Shayegan Omidshafiei1, Daniel Hennes1, Dustin Morrill13, Paul Muller1, Timo Ewalds1, Ryan Faulkner1, János Kramár1, Bart De Vylder1, Brennan Saeta2, James Bradbury2, David Ding1, Sebastian Borgeaud1, Matthew Lai1, Julian Schrittwieser1, Thomas Anthony1, Edward Hughes1, Ivo Danihelka1, Jonah Ryan-Davis2

1DeepMind, 2Google, 3University of Alberta

What it says

“OpenSpiel is a collection of environments and algorithms for research in general RL and search/planning in games.” It contains 28 environments (shown above) that have various different properties: perfect or imperfect information, single-agent or multi-agent, etc. It also contains 22 baseline algorithms that spans a wide field of RL such as tree search, tabular RL, deep RL, and multi-agent RL. The environments are implemented in C++ and wrapped in Python, and algorithms are implemented in either C++ or Python. For visualization and evaluation of multi-agent RL, OpenSpiel offers phase portraits (Section 3.3.1) and $\alpha$-Rank (Section 3.3.2).

Read more

External resources

Interactive Machine Reading Comprehension

Xingdi Yuan1*, Jie Fu2*, Marc-Alexandre Cote1, Yi Tay3, Christopher Pal2, Adam Trischler1

1Microsoft Research, Montreal, 2Polytechnique Montreal, Mila, 3Nanyang Technological University

What it says

Question Answering (QA) is a problem where the model must output a correct answer given a question and a supporting paragraph containing the answer. In the web, the text to search the answer for could be very large, so the authors instead propose an “interactive” variant. Unlike the default “static” situation where the full text is given, in the interactive variant only a part of the text is given and the agent must select actions to discover sentences with relevant information. During this information gathering phase, the agent can choose to move to the previous or next sentence, to jump to a next occurrence of a token (i.e. Ctrl+F), or to stop. When a stop action is given, the agent enters the question answering phase in which it outputs the head and tail position of the answer in the supporting text.

The authors build interactive versions of SQuAD v1.1 and NewsQA and test their methods and report F1 scores of 0.666 for iSQuAD and 0.367 for iNewsQA (best numbers among variants, Table 4). The authors also report 0.716 and 0.632 for the “F1 info score,” which only tests whether the agent stopped at the correct segment of the text during the information gathering phase.

Read more

External resources

More exciting news in RL this week:


Survey of…

Applying RL for: