Single Agent Actor-Critic

We implement and train a single-agent actor-critic agent based on the CNN+LSTM+Actor/Critic architecture.

We perform hyperparameter optimization on 4 key parameters:

1. Temperature
2. Learning rate
3. Gradient Clipping
4. Backprop methodologies (TBPTT or BPTE)

In addition we benchmark our agent against A3C 1,4 and 16 agents implemented by ikostrikov:

https://github.com/ikostrikov/pytorch-a3c

An overview of our results:

Pong
Our agent achieves:

• human performance (9.3) after 4467 episodes
• max performance (18.4) after 56896 episodes

Breakout
Our agent achieves:

• human performance (31.8) after 22201 episodes
• max performance (253.5) after 274616 episodes