ppo-LunarLander-v3 is an open-source reinforcement learning model trained to solve the LunarLander-v3 environment using the PPO algorithm and stable-baselines3. It provides pretrained weights and code for researchers and developers to benchmark or extend RL experiments. Ideal for those working in reinforcement learning or educational settings.
Ppo LunarLander is an Other AI product. It focuses on training and evaluating reinforcement learning agents for the LunarLander-v3 environment without building models from scratch. It is built as an open-source project for machine learning researchers. Ppo LunarLander is open source under the MIT license. The product ships for the web and the command line.
It is developed by modeliqi, and the product first shipped in 2019. Development happens publicly on GitHub with 13.6k stars and 10 commits in the last 90 days. It operates in a well-populated space: PulseGate tracks 9 similar tools. Key capabilities include reinforcement learning, lunarLander-v3 support, and stable-baselines3 compatible.
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