CLAIRE-Labo/no-representation-no-trust
Codebase to fully reproduce the results of "No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO" (Moalla et al. 2024). Uses TorchRL and provides extensive tools for studying representation dynamics in policy optimization.
This project provides the tools and codebase to understand why Reinforcement Learning (RL) agents, specifically those using Proximal Policy Optimization (PPO), sometimes fail to learn effectively and experience performance collapse. It helps RL researchers analyze how the internal 'representation' of an agent changes over time. You input experimental parameters for PPO agents in environments like Atari or MuJoCo, and it outputs detailed logs and plots showing agent performance and internal representation dynamics, helping you diagnose and mitigate learning failures.
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Use this if you are an RL researcher or practitioner investigating stability and plasticity issues in PPO-based agents and need to analyze the underlying causes of performance collapse related to internal representations.
Not ideal if you are looking for a general-purpose RL library to train agents without a specific focus on analyzing representation dynamics and learning stability.
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Python
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MIT
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Last pushed
Nov 20, 2024
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