Breaking the 'Greedy' Trap: Why a 'Burn-in' Phase Matters in Reinforcement Learning
Reinforcement learning (RL) is a powerful paradigm for training agents to make decisions in complex environments. However, one common pitfall that can hinder optimal performance is the 'greedy' exploration problem. When an agent is too focused on exploiting the current best action, it may overlook valuable information in the environment, leading to suboptimal results.
The Problem with Greedy Exploration
Greedy exploration occurs when an agent prioritizes immediate rewards over long-term benefits, resulting in a myopic decision-making process. This can lead to:
- Overestimation of the current policy's value
- Ignoring potential risks or challenges
- Failing to learn from suboptimal experiences
Introducing a 'Burn-in' Phase
To mitigate the greedy exploration problem, a 'burn-in' phase can be implemented, where the agent learns a robust initial policy before switching to exploration-ex...
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