What is IMPALA?
IMPALA, which stands for Importance Weighted Actor Learner Architecture, is an off-policy actor-critic framework. The framework separates acting from learning and allows learning from experience trajectories using V-trace. IMPALA is different from other agents like A3C because it communicates trajectories of experience to a centralized learner rather than gradients with respect to the parameters of the policy to a central parameter server. The decoupled architecture of IMPALA allows it to achieve very high throughput, but it also results in off-policy learning.
How Does IMPALA Work?
IMPALA uses off-policy V-trace actor-critic algorithm for learning. The framework separates the actor that collects experience and the critic that learns from the experience. IMPALA actors generate experience trajectories that are communicated to a centralized learner. The learner updates the policy using the V-trace algorithm that corrects for off-policy learning.
The V-trace algorithm mitigates the discrepancy between the policy used to generate a trajectory and the policy on the learner. This discrepancy can be harmful to the learning process. IMPALA also parallelizes all the time-independent operations using GPU to enhance the learning throughput.
Why is IMPALA Useful?
IMPALA's decoupled architecture makes it very useful for high-throughput reinforcement learning. It can handle very large experience spaces effectively, allowing for efficient learning. Using V-trace further improves the stability of the learning process by correcting for off-policy learning. The algorithm can handle large numbers of agents and scales well with increasing numbers of CPUs or GPUs.
IMPALA can handle different types of objectives, including continuous control, Atari games, and robotics, among others. The algorithm can also learn from both discrete and continuous action spaces. IMPALA can be useful for real-world applications where high-throughput, stable, and efficient learning is required.
IMPALA is an off-policy actor-critic framework that decouples acting from learning and uses V-trace for learning from experience trajectories. The decoupled architecture allows for high-throughput reinforcement learning, making it useful for real-world applications. The V-trace algorithm used in IMPALA mitigates the discrepancy between the policy used to generate a trajectory and the policy on the learner, resulting in more stable learning. IMPALA can handle different types of objectives, including continuous control, Atari games, and robotics, among others, and can learn from both discrete and continuous action spaces.
