Tag: Reinforcement Learning | NVIDIA Developer Blog

Reinforcement Learning

AI / Deep Learning

gradient-ascent

Enhancing Sample Efficiency in Reinforcement Learning with Nonparametric Methods

By Samuele Tosatto | September 1, 2020

AI / Deep Learning

automl-controller-network-training-flow

Powering AutoML-enabled AI Model Training with Clara Train

By Yan Cheng, Dong Yang, Holger Roth and Daguang Xu | April 15, 2020

Artificial Intelligence

Figure 1: Value iteration constructs the value function over all states over time. Here each square is a state: S is the start state, G the goal state, T squares are traps, and black squares cannot be entered. In value iteration we initialize the rewards (traps and goal state) and then these reward values spread over time until an equilibrium is reached. Depending on the penalty value on traps and the reward value for the goal different solution patterns might emerge; the last two grids show such solution states.

Deep Learning in a Nutshell: Reinforcement Learning

By Tim Dettmers | September 8, 2016

Artificial Intelligence

open-ai

Train Your Reinforcement Learning Agents at the OpenAI Gym

By Mark Harris | April 27, 2016