Reinforcement learning (RL) can be applied to partially observable environments using a technique called Markov Decision Processes (MDPs). MDPs are a mathematical framework that models a decision-making process where the outcome of an action is uncertain, and the next state depends on both the previous state and the action taken by the decision-maker.
In a partially observable environment, the agent may have incomplete information about the state of the environment. For example, in a game like Poker, the agent cannot see the other players’ cards and therefore does not know the exact state of the game.
To address this, RL algorithms can use a technique called "belief state" to maintain a probability distribution over the state space. The belief state represents the agent’s current knowledge about the state of the environment. The agent updates its belief state after each action based on the observations it receives
There are several RL algorithms that can operate in partially observable environments. One popular algorithm is called POMDP (Partially Observable Markov Decision Process) which extends the MDP framework to include belief states and observations. POMDPs provide a unified framework for planning and decision making in partially observable environments.
Another technique used in RL is the Kalman filter, which can be used to estimate the state of the environment based on noisy sensor data. The Kalman filter is often used in robotics to localize the agent in an uncertain environment.
Monte Carlo tree search (MCTS) is another algorithm that can handle partially observable environments. MCTS works by simulating a large number of possible future trajectories and selecting the action that maximizes the expected return based on those simulations.
In summary, RL algorithms can successfully handle partially observable environments by using a combination of techniques such as belief state, Kalman filters, and MCTS. These techniques allow the agent to make effective decisions in complex and uncertain environments, even when the agent has incomplete information about the state of the environment.