Asynchronous Advantage Actor-Critic (A3C)
Advanced & Deep RL DS practice problem on Onlearn.
Difficulty: hard.
Topics: Understanding Asynchronous Advantage Actor-Critic (A3C) Architecture, Entropy Regularization, Asynchronous Gradient Updates, Generalized Advantage Estimation (GAE), Shared Global Network Parameters, Experience Buffering, Reinforcement Learning, Deep Learning, Optimization Theory, Parallel Computing, Stochastic Processes, Policy Gradient Methods, Temporal Difference Learning, Advantage Estimation, Actor-Critic Architectures, Distributed Systems.
Implement a simplified A3C worker process structure. You are required to create a class 'A3CWorker' that encapsulates the logic for interacting with an environment, calculating the advantage (A = Q V), and computing the loss function based on the Actor Critic policy gradient theorem. The loss should combine the policy gradient loss, the value loss, and an entropy regularization term to encourage exploration.