In the context of handling partial observability in the multi-agent setting, the proposed algorithms can be extended by incorporating techniques from Partially Observable Markov Decision Processes (POMDPs). POMDPs are an extension of MDPs that deal with situations where agents do not have full visibility of the environment states.
One approach to handle partial observability is to introduce belief states for each agent, representing the probability distribution over the possible states of the environment. Each agent can maintain its belief state based on its observations and update it using Bayesian inference. By incorporating belief states into the algorithm, agents can make decisions based on the most likely states of the environment given their observations.
Another technique is to use Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks to model the agent's memory and history of observations. By utilizing the sequential nature of RNNs, agents can learn to make decisions based on a sequence of observations, effectively handling partial observability.
Furthermore, techniques such as Monte Carlo Tree Search (MCTS) can be employed to explore the space of possible actions and observations, allowing agents to make informed decisions even in partially observable environments.