In the last
yarlp blog post, I ran Double Deep Q-Learning on Atari, which took around 1-1.5 days to train per Atari environment for 40M frames. I wanted to implemented something faster, namely A2C (Advantage Actor Critic). It’s related to A3C by Mnih et al, 2016, without the asynchronous part.
A2C is pretty simple:
- Create $N_e$ (16) environments that will execute in parallel.
- Execute rollouts for $N_s$ (5) time-steps in each environment.
- Calculate the discounted $N_s$-step returns, which we call $R$.
- Calculate the advantage with $A(s) = V(s) - R$ where $V(s)$ is a function that takes states as inputs and outputs a prediction for $R$
- Update a deep neural network according to the following loss: $\sum (R - V(s))^2 - log(\pi(s)) A(s)$, where $\pi(s)$ is the policy network that shares weights with $V(s)$ but outputs actions.
A2C is horribly sample inefficient, meaning it doesn’t learn as quickly per frame compared to DDQN. But A2C can train on 40M frames within a couple of hours with 16 threads compared to 1-1.5 days, solving enviornments like Pong and Breakout a lot faster. The learning curves by frames (left) and by wall-clock time (right) are shown below.
I added an implementation of A2C to
You may be thinking, why not add experience replay to A2C to make it more sample efficient? And while we’re at it, why not add some trust-region update as well? Turns out this is roughly known as ACER, which I don’t think I’ll implement. Ben Recht’s posts on An Outsider’s Tour of of Reinforcement Learning, makes many of these methods seem like bag of tricks algorithms. In fact, Recht’s students recently published a paper showing that random search with linear policies can perform as well as if not better than TRPO. ¯\(ツ)/¯