Policy Gradient Methods for Non-Markovian Reinforcement Learning
📰 ArXiv cs.AI
Learn to apply policy gradient methods to non-Markovian reinforcement learning using internal state updates to handle interaction history dependence
Action Steps
- Implement an internal state update mechanism to track past observations and actions
- Apply policy gradient methods to optimize the agent's policy in non-Markovian decision processes
- Update the agent's internal state recursively to provide a compact summary of the interaction history
- Evaluate the performance of the policy gradient method in NMDPs using appropriate metrics
- Compare the results with other approaches that treat agent state dynamics as fixed or learn it via predictive objectives
Who Needs to Know This
Researchers and engineers working on reinforcement learning and decision-making under non-Markovian conditions can benefit from this knowledge to improve their models
Key Insight
💡 Internal state updates can effectively handle interaction history dependence in non-Markovian decision processes
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🤖 Apply policy gradient methods to non-Markovian RL using internal state updates! 📈
Key Takeaways
Learn to apply policy gradient methods to non-Markovian reinforcement learning using internal state updates to handle interaction history dependence
Full Article
Title: Policy Gradient Methods for Non-Markovian Reinforcement Learning
Abstract:
arXiv:2605.10816v1 Announce Type: cross Abstract: We study policy gradient methods for reinforcement learning in non-Markovian decision processes (NMDPs), where observations and rewards depend on the entire interaction history. To handle this dependence, the agent maintains an internal state that is recursively updated to provide a compact summary of past observations and actions. In contrast to approaches that treat the agent state dynamics as fixed or learn it via predictive objectives, we pro
Abstract:
arXiv:2605.10816v1 Announce Type: cross Abstract: We study policy gradient methods for reinforcement learning in non-Markovian decision processes (NMDPs), where observations and rewards depend on the entire interaction history. To handle this dependence, the agent maintains an internal state that is recursively updated to provide a compact summary of past observations and actions. In contrast to approaches that treat the agent state dynamics as fixed or learn it via predictive objectives, we pro
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