AI Rewind 2020: Trends in Reinforcement Learning with Pablo Samuel Castro
EPISODE 443
|
DECEMBER
30,
2020
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About this Episode
Today we kick off our annual AI Rewind series joined by friend of the show Pablo Samuel Castro, a Staff Research Software Developer at Google Brain.
Pablo joined us earlier this year for a discussion about Music & AI, and his Geometric Perspective on Reinforcement Learning, as well our RL office hours during the inaugural TWIMLfest. In today's conversation, we explore some of the latest and greatest RL advancements coming out of the major conferences this year, broken down into a few major themes, Metrics/Representations, Understanding and Evaluating Deep Reinforcement Learning, and RL in the Real World. This was a very fun conversation, and we encourage you to check out all the great papers and other resources available below.
We want to hear from you! Send your thoughts on the year that was 2020 below in the comments, or via Twitter at @samcharrington or @twimlai.
To follow along with the 2020 AI Rewind Series, head over to the series page!
About the Guest
Pablo Samuel Castro
Google Brain
Connect with Pablo
Resources
- Music & AI Plus A Geometric Perspective on Reinforcement Learning
- Paper: Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
- Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
- State Alignment-based Imitation Learning
- Contrastive Learning of Structured World Models
- CURL: Contrastive Unsupervised Representations for Reinforcement Learning
- Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
- Planning to Explore via Self-Supervised World Models
- Representations for Stable Off-Policy Reinforcement Learning
- Dream to Control: Learning Behaviors by Latent Imagination
- Fast Task Inference with Variational Intrinsic Successor Features
- Model Based Reinforcement Learning for Atari
- Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
- Latent World Models For Intrinsically Motivated Exploration
- Measuring the Reliability of Reinforcement Learning Algorithms
- Revisiting Fundamentals of Experience Replay
- Behaviour Suite for Reinforcement Learning
- Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
- Implementation Matters in Deep RL: A Case Study on PPO and TRPO
- What Can Learned Intrinsic Rewards Capture?
- Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
- Munchausen Reinforcement Learning
- B-Suite
- Jax
