Model-based reinforcement learning (RL) algorithms use models to predict the outcomes of actions taken in an environment. These models can represent either the dynamics of the environment or the rewards that result from actions. Common model-based RL algorithms typically leverage these predictions to improve decision-making, often combining exploration with planning. Three notable examples are Dyna-Q, AlphaZero, and Model-Based Policy Optimization (MBPO).
Dyna-Q is a classic approach that integrates model learning, planning, and online learning. In Dyna-Q, an agent maintains a model of the environment's dynamics and uses it to simulate experiences. When the agent takes an action, the model predicts the next state and the associated reward. This prediction allows the agent to update its value function or policy based on imagined experiences, while also learning from real interactions with the environment. By doing this, Dyna-Q strikes a balance between learning from actual data and improving its understanding through simulated experiences.
AlphaZero is another prominent model-based method, particularly in the context of games like chess and Go. Unlike Dyna-Q, AlphaZero leverages a neural network to approximate the environment's value function and policy. The algorithm plays games against itself to generate data, which is then used to update the network. This self-play not only serves as a way to improve the model but also efficiently explores potential strategies through extensive simulations. In contrast, Model-Based Policy Optimization (MBPO) uses a learned model of the environment combined with policy optimization methods like Proximal Policy Optimization (PPO). MBPO focuses on using the model to generate training data, which is then employed to optimize the policy more effectively, allowing for more informed updates that can lead to better performance in complex sequential decision-making tasks.
Overall, model-based RL algorithms enable more efficient exploration and decision-making by using models to guide learning. They are especially useful in environments where sample efficiency is crucial, as they can make the most out of both real and simulated experiences to improve learning outcomes.