In this paper, we explore the use of discrete world models to master Atari games. The authors, Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba, aim to improve upon previous methods that used continuous state representations. They propose a new approach called "Mastering atari with Discrete World Models" (MAD WM).
Discrete World Models
A world model is a probabilistic model of the environment. In this case, it predicts the next state given the current state and action. Previous methods used continuous state representations, which led to inefficient sampling and slow learning. The authors propose using discrete state representations instead, which can be more efficient and effective.
Advantages of Discrete World Models
- Improved sample efficiency: Using discrete state representations allows for faster exploration of the environment, leading to more efficient learning.
- Transfer learning: Once the model has learned the relationship between states and actions in one task, it can be easily adapted to other tasks without additional training. This makes it easier to apply the knowledge gained from one game to another.
- Better generalization: The discrete state representations allow for a clearer understanding of the relationships between states, leading to better generalization to new situations.
Trajectory Imagination
To generate an imagined trajectory, the authors use a diffusion model that takes as input a starting state and outputs a sequence of actions and rewards. The diffusion model is trained using reinforcement learning techniques and learns to predict the next action given the current state. Once the model has been trained, it can be used to generate imagined trajectories for any given starting state.
Generating Trajectories
The process of generating an imagined trajectory involves sampling a starting state and then iteratively sampling actions from the policy until a desired length trajectory is reached. At each step, the model predicts the next action given the current state and outputs it as the new state. This process is repeated until the desired length trajectory has been generated.
Autoregressive Sampling
Autoregressive sampling is used to generate synthetic on-policy trajectories in a single pass of diffusion. The model predicts each action in the sequence conditioned on the previous state, allowing for efficient generation of entire on-policy trajectories. This approach is more efficient than other methods that require multiple passes through the data or use off-policy sampling techniques.
Conclusion
In this paper, we have explored the use of discrete world models to master Atari games. The authors proposed a new approach called "Mastering atari with Discrete World Models" (MAD WM), which uses discrete state representations to improve sample efficiency and facilitate transfer learning. Additionally, the authors introduced the concept of trajectory imagination, which allows for efficient generation of imagined trajectories using a diffusion model. Overall, this work has the potential to improve the efficiency and effectiveness of reinforcement learning algorithms in a variety of applications.
