Atari benchmark 7/28/2019

[kengz]

Atari benchmark

This is a benchmark ran using the current master branch. All the results are shown below and the data folders including the metrics and models are uploaded to the SLM Lab public Dropbox with file prefix PR396-.

To Reproduce

1. JSON spec: See the spec/benchmark folder
2. git SHA (contained in the file above): 8360612e05985210dcf84de2f8302440a5c8d81c

Env. \ Alg.	A2C (GAE)	A2C (n-step)	PPO	DQN	DDQN+PER
Breakout graph	389.99 graph	391.32 graph	425.89 graph	65.04 graph	181.72 graph
Pong graph	20.04 graph	19.66 graph	20.09 graph	18.34 graph	20.44 graph
Qbert graph	13,328.32 graph	13,259.19 graph	13,691.89 graph	4,787.79 graph	11,673.52 graph
Seaquest graph	892.68 graph	1,686.08 graph	1,583.04 graph	1,118.50 graph	3,751.34 graph

Terminology

• A2C (GAE): Advantage Actor-Critic with GAE as advantage estimation
• A2C (n-step): Advantage Actor-Critic with n-step return as advantage estimation
• DDQN+PER: Double Deep Q-Learning with Prioritized Experience Replay
• DQN: Deep Q-learning
• PPO: Proximal Policy Optimization

BeamRider	Breakout	KungFuMaster	MsPacman
Pong	Qbert	Seaquest	Sp.Invaders