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(ICRA 2024)Contrastive Initial State Buffer for Reinforcement Learning
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https://www.youtube.com/watch?v=RB7mDq2fhho (ICRA 2024)Contrastive Initial State Buffer for Reinforcement Learning Abstract In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples. While recent works have been effective in leveraging past experiences for policy updates, they often overlook the potential of reusing past experiences for data collection. Independent of the underlying RL algorithm, we introduce the concept of a Contrastive Initial State Buffer, which strategically selects states from past experiences and uses them to initialize the agent in the environment in order to guide it toward more informative states. We validate our approach on two complex robotic tasks without relying on any prior information about the environment: (i) locomotion of a quadruped robot traversing challenging terrains and (ii) a quadcopter drone racing through a track. The experimental results show that our initial state buffer achieves higher task performance than the nominal baseline while also speeding up training convergence. Content This repository contains the code for the Contrastive Initial State Buffer (CL-Buffer), which can be installed as a Python library. The repository does not contain the code for training an RL agent in an environment (Drone Racing or Legged Locomotion). However, with the given toy example (toy_example.py), it is straightforward to implement the CL-Buffer in an existing RL framework. https://rpg.ifi.uzh.ch/docs/ICRA24_Messikommer.pdf https://github.com/uzh-rpg/cl_initial_buffer
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