Deep Reinforcement Learning and its Neuroscientific Implications

Notes from Deep Reinforcement Learning and Its Neuroscientific Implications

Backward — Learning from a Single Demonstration

Discussion on a curriculum learning algorithm that gradually learns a policy gradient algorithm on Montezuma’s Revenge

The Mirage of Action-Dependent Baselines

Analysis on action-dependent baselines

Self-Tuning Reinforcement Learning

A self-tuning reinforcement learning algorithm for IMPALA.

V-trace

Theoretical analysis of the V-trace target.

Retrace(𝝀)

A theoretical analysis of the Retrace(𝝀) algorithm.

M-RL — Munchausen Reinforcement Learning

Discussion on Munchausen Reinforcement Learning, which considers policy in Bellman updates.

Behavior Priors for Kl regularized Reinforcement Learning

Discussion on behavior priors for KL regularized reinforcement learning

A Unified View of KL-Regularized RL

We present a unified view of policy gradient and soft Q-learning.

Hide and Seek

Discussion on an agent developed by OpenAI et al. that exhibits several emergent strategies in hide-and-seek environment.