Author Archives: Anurag Upadhyaya - Page 3
Various Benchmarks have played an important role in various domains of machine learning such as MNIST (LeCun et al., 1998), Caltech101 (Fei-Fei et al., 2006), CIFAR (Krizhevsky & Hinton, 2009), ImageNet (Deng et al., 2009). However, there is a lack of standardized testbed for Reinforcement Learning algorithms. Various benchmarks released by OpenAI such as Procgen,…
The post A Hands-On Guide on Training RL Agents on Classic Control Theory Problems appeared first on Analytics India Magazine.
In the last two articles, the focus has been to measure the generalization performance of Reinforcement learning agents using Gym Retro and Procgen environments. Both these environments used 2-D environments and were limited to the first player arcade gaming experience. However, procgen is procedurally generated but it still has the limitations of 2-D and hardly…
The post Vision, Control, Planning, and Generalization in RL appeared first on Analytics India Magazine.
In Reinforcement learning, the generalization of the agents is benchmarked on the environments they have been trained on. In a supervised learning setting, this would mean testing the model using the training dataset. OpenAI has open-sourced Procgen-benchmark emphasizing the generalization for RL agents as they struggle to generalize in new environments. Procgen consists of 16…
The post Generalization in Reinforcement Learning – Exploration vs Exploitation appeared first on Analytics India Magazine.
Reinforcement learning is continuously being made easy by OpenAI. On their, mission to develop and promote friendly AI that helps humanity, OpenAI released Stable-Baselines. It was created by Robotics Lab U2IS (INRIA Flowers Team) at ENSTA Paris with a goal to provide Scikit Learn like coding structure to give a unified style to program the…
The post Hands-On Guide To Train RL Agents using Stable BaseLines on Atari Gym Environment appeared first on Analytics India Magazine.
The goal of any Reinforcement learning agent is to maximize the cumulative rewards based on the goals for the provided environment. The learner is not told which actions to take but must discover which actions yield the most rewards by trying them. To develop such an agent, it is obligatory to allow the agents to…
The post Hands-on Guide To Creating RL Agents Using OpenAI Gym Retro appeared first on Analytics India Magazine.