Reinforcement Learning
Playing hard exploration games by watching YouTube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, Nando de Freitas
Deep reinforcement learning methods traditionally struggle withtaskswhere environment rewards are particularly sparse.
Large Language Models Can Implement Policy Iteration Ethan Brooks 1, Logan Walls 2, Richard L. Lewis
Gradient techniques are inherently slow, sacrificing the "few-shot" quality
Constrained Cross-Entropy Method for Safe Reinforcement Learning
Neural Information Processing Systems http://nips.cc/
e92381dba235a8309f08ce46376189a9-Paper-Conference.pdf
The transition dynamics simply mixes an action and a random sampled latent. It then applies an exponential moving average for temporal persistency, the resulting latent is decoded to image using pretrained generator.
Distributional Policy Optimization: An Alternative Approach for Continuous Control
Chen Tessler, Guy Tennenholtz, Shie Mannor
Wesuggestanoveldistributional framework, able to represent arbitrary distribution functions over the continuous actionspace.
