The Markovian assumption is a fundamental concept in Reinforcement Learning (RL) that assumes that the current state of an agent contains all the information needed to determine its next action, and that the probability distribution of transitioning from the current state to any next state depends only on the current state and action taken. However, in many real-world environments, this assumption does not hold, leading to limitations of traditional RL algorithms. Here are some of the limitations:
1. Markov Assumption Limits Sequential Decision Making: The Markov assumption only considers the current state, but not the history leading to it; thus, it limits the ability of agents to make decisions based on past experiences. For example, in playing a game like chess, a player’s strategy may depend on the opening moves already played.
2. Non-stationarity of Environments: In many environments, the probability distribution of transitioning from one state to another is not fixed over time, i.e., it is non-stationary. In these cases, the Markov assumption cannot capture the underlying patterns and can lead to suboptimal results.
3. Partial Observability: In some environments, the agent may not have access to all the information about the current state. This non-Markovian property is called Partial observability, and it makes it hard for the agent to determine the best actions to take since it is uncertain about the current state.
Alternative frameworks can help address non-Markovian environments. Here are two of the most popular frameworks:
1. Partially Observable Markov Decision Processes (POMDPs): POMDPs are a generalization of Markov decision processes (MDPs) that can handle partially observable environments. In a POMDP, the agent does not observe the true state but instead receives an observation that provides partial information about the true state. The agent must then use a belief state to represent its uncertainty about the true state and update it with each observation. POMDPs can provide a more robust RL framework for realistic environments with limited information.
2. Recurrent Neural Networks (RNNs): RNNs are neural networks that can capture sequential dependencies. They achieve this by allowing information from previous states to be fed into the current state. RNNs can help agents make decisions based on past experiences more effectively by capturing the history leading to the current state. For example, in playing a game like chess, an RNN could use the opening moves to adapt the player’s strategy throughout the game.
In conclusion, the Markov assumption is a fundamental concept in RL, but it has limitations that can impact the effectiveness of traditional RL algorithms. Alternative frameworks like POMDPs and RNNs can help address non-Markovian environments and improve the robustness of RL algorithms in real-world applications.