CARL (Context Adaptive RL)
Introduced by Benjamins et al. in CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments. It's designed to test your agent's generalization capabilities in all scenarios where intra-task generalization is important.
Benchmarks include:
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OpenAI gym classic control suite extended with several physics context features like gravity or friction
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OpenAI gym Box2D BipedalWalker, LunarLander and CarRacing, each with their own modification possibilities like new vehicles to race
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All Brax locomotion environments with exposed internal features like joint strength or torso mass
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Super Mario (TOAD-GAN), a procedurally generated jump'n'run game with control over level similarity
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RNADesign, an environment for RNA design given structure constraints with structures from different datasets to choose from
Description from: CARL
Papers
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