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RL Weekly 22: Unsupervised Learning for Atari, Model-based Policy Optimization, and Adaptive-TD

RL Weekly 22: Unsupervised Learning for Atari, Model-based Policy Optimization, and Adaptive-TD

This week, we first look at ST-DIM, an unsupervised state representation learning method from MILA and Microsoft Research. We also check UC Berkeley's new policy...

RL Weekly 21: The interplay between Experience Replay and Model-based RL

RL Weekly 21: The interplay between Experience Replay and Model-based RL

This week, we introduce three papers on replay-based RL and model-based RL. The first paper introduces SoRB, a way to combine experience replay and planning....

RL Weekly 20: Minecraft Competition, Off-policy Policy Evaluation via Classification, and Soft-attention Agent for Interpretability

RL Weekly 20: Minecraft Competition, Off-policy Policy Evaluation via Classification, and Soft-attention Agent for Interpretability

This week, we introduce MineRL, a new RL competition using human priors to solve Minecraft. We also introduce OPE, a method of off-policy evaluation through...

RL Weekly 19: Curious Object-Based Search Agent, Multiplicative Compositional Policies, and AutoRL

RL Weekly 19: Curious Object-Based Search Agent, Multiplicative Compositional Policies, and AutoRL

This week, we introduce combining unsupervised learning, exploration, and model-based RL; learning composable motor skills; and evolving rewards.

RL Weekly 18: Survey of Domain Randomization Techniques for Sim-to-Real Transfer, and Evaluating Deep RL with ToyBox

RL Weekly 18: Survey of Domain Randomization Techniques for Sim-to-Real Transfer, and Evaluating Deep RL with ToyBox

This week, we introduce a survey of Domain Randomization Techniques for Sim-to-Real Transfer and ToyBox, a suite of redesigned Atari Environments for experimental evaluation of...

RL Weekly 17: Information Asymmetry in KL-regularized Objective, Real-world Challenges to RL, and Fast and Slow RL

RL Weekly 17: Information Asymmetry in KL-regularized Objective, Real-world Challenges to RL, and Fast and Slow RL

In this issue, we summarize the use of information asymmetry in KL regularized objective to regularize the policy, the challenges of deploying deep RL into...