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Found 3 papers, 2 papers with code
Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning
We evaluate our approach with three state-of-the-art DRL algorithms and find that differential image encoding reduces the memory footprint by as much as 14. 2x and 16. 7x across tasks from the Atari 2600 benchmark and the DeepMind Control Suite (DMC) respectively.
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion
Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing complex robotic behaviors.
A Comparison of Action Spaces for Learning Manipulation Tasks
Designing reinforcement learning (RL) problems that can produce delicate and precise manipulation policies requires careful choice of the reward function, state, and action spaces.
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