- Published on February 14, 2020
Hands-on Guide To Creating RL Agents Using OpenAI Gym Retro
- By Anurag Upadhyaya
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 experience a diverse set of environments so they can develop problem-solving skills that can be leveraged in different tasks.
The RL research community is keenly focused on using Games as an environment to train agents for a diverse set of tasks. Last year, DeepMind released OpenSpiel, with a purpose to promote the multi-agent RL systems across different games with an emphasis to learn and not to compete.
As any game is learned with experience, it provides the perfect train
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Anurag Upadhyaya
Experienced Data Scientist with a demonstrated history of working in Industrial IOT (IIOT), Industry 4.0, Power Systems and Manufacturing domain.
I have experience in designing robust solutions for various clients using Machine Learning, Artificial Intelligence, and Deep Learning.
I have been instrumental in developing end to end solutions from scratch and deploying them independently at scale.
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