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RL Weekly 27: Diverse Trajectory-conditioned Self Imitation Learning and Environment Probing Interaction Policies

RL Weekly 27: Diverse Trajectory-conditioned Self Imitation Learning and Environment Probing Interaction Policies

This week, we look at a self imitation learning method that imitates diverse past experience for better exploration. We also summarize an environment probing policy...

RL Weekly 26: Transfer RL with Credit Assignment and Convolutional Reservoir Computing for World Models

RL Weekly 26: Transfer RL with Credit Assignment and Convolutional Reservoir Computing for World Models

This week, we summarize a new transfer learning method using the Transformer reward model, and a world model controller that does not require training the...

Setting up code-server on GCP: VSCode on Browser for Remote Work!

Setting up code-server on GCP: VSCode on Browser for Remote Work!

Visual Studio Code (VS Code) is a great code editor, but it cannot be used remotely... or can it? Code-server is VS Code running on...

RL Weekly 25: Replacing Bias with Adaptive Methods, Batch Off-policy Learning, and Learning Shared Model for Multi-task RL

RL Weekly 25: Replacing Bias with Adaptive Methods, Batch Off-policy Learning, and Learning Shared Model for Multi-task RL

In this issue, we focus on replacing inductive bias with adaptive solutions (DeepMind), learning off-policy from expert experience (Google Brain), and learning a shared model...

RL Weekly 24: Benchmarks for Model-based RL and Bonus-based Exploration Methods

RL Weekly 24: Benchmarks for Model-based RL and Bonus-based Exploration Methods

This week, we summarize two benchmark papers. The first paper benchmarks 11 model-based RL algorithms in 18 continuous control environments, and the second paper benchmarks...

RL Weekly 23: Decentralized Hierarchical RL, Deep Conservative Policy Iteration, and Optimistic PPO

RL Weekly 23: Decentralized Hierarchical RL, Deep Conservative Policy Iteration, and Optimistic PPO

This week, we first introduce a ensemble of primitives without a high-level meta-policy that can make decentralized decisions. We then look at an deep learning...