Code available in Github

Videos of last saved policy in standard Atari games. The extrinsic rewards is used to measure the performance. It is important to note that the extrinsic reward is only used for evaluation. Our method performs best in most games.

Videos of last saved policy in Atari games with sticky actions. This kind of environment is used to evaluate the robustness of exploration methods by introducing stochasticity in Atari games. In time step, the environment will execute the agent’s previous action again with probability 0.25.

The learning is more challenging in sticky Atari games. The performance of VDM is less affected by sticky actions by using the latent variables to encode the stochasticity of the environment.

The Super Mario is a popular game that has several levels with different scenarios. We evaluate the transfer ability of pure-exploration policy by using the policy learned from Level-1 to adapt to other levels