rl_games and Practical_RL
rl_games is a practical implementation framework while Practical_RL is an educational course that teaches RL concepts—they are complements, as the course could use rl_games as a reference implementation or hands-on tool for students learning reinforcement learning algorithms.
About rl_games
Denys88/rl_games
RL implementations
This project helps robotics engineers and researchers train robots and intelligent agents to perform complex tasks using reinforcement learning. You can input simulated environments or real-world robotic data and get optimized control policies for dexterous manipulation, locomotion, or multi-agent coordination. It's ideal for those developing AI for robotics, autonomous systems, or game AI.
About Practical_RL
yandexdataschool/Practical_RL
A course in reinforcement learning in the wild
This course helps aspiring practitioners understand how to develop intelligent agents that learn optimal behaviors through trial and error in various environments. It takes learners from foundational concepts like decision processes and value-based methods to advanced topics such as deep reinforcement learning, policy gradient methods, and model-based RL. It's designed for anyone interested in building systems that can make sequential decisions to achieve goals, like autonomous robots, game AI, or resource management.
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