"Active Reinforcement Learning Strategies for Offline Policy Improvement"

AI agent that efficiently collects new experiences by focusing on truly uncertain regions.

Making it efficient by focusing on what it doesn't know yet.

This paper proposes an efficient method to collect additional data in reinforcement learning by identifying uncertain regions using representation learning and targeted exploration strategies.

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https://arxiv.org/abs/2412.13106

🤖 Original Problem:

→ In real-world reinforcement learning, collecting new training data is expensive and often restricted

→ Need to efficiently use limited interaction budget while leveraging existing offline data

→ Traditional approaches waste interactions by collecting redundant data

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🔍 Solution in this Paper:

→ Uses ensemble of representation models to estimate uncertainty in different regions of state space

→ Identifies areas where agent has low confidence based on existing offline data

→ Employs two-stage approach - first reaches uncertain regions, then explores them intelligently

→ Combines state and action encoders to learn meaningful representations for uncertainty estimation

→ Uses epsilon-greedy strategy with uncertainty-based action selection

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💡 Key Insights:

→ Representation-aware uncertainty helps identify truly novel regions needing exploration

→ Two-stage policy handles environments with restricted initial states effectively

→ Intelligent data collection significantly reduces required interactions compared to naive fine-tuning

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📊 Results:

→ Reduces additional online interactions by up to 75% compared to baselines

→ Achieves better final performance across maze navigation, locomotion tasks and autonomous driving

→ Particularly effective when offline dataset has incomplete coverage of important state regions