Advancing Reinforcement Learning: Model-Based Approach for Non-Markovian Environments

The research explores a critical challenge in reinforcement learning: how to handle non-Markovian reward decision processes effectively. This is significant because real-world environments often lack the Markov property, making standard RL techniques less reliable.

• •Investigates model-based reinforcement learning.
• •Addresses the complexities of non-Markovian environments.
• •Applicable to a range of decision-making problems.