Translations:Diffusion Models Are Real-Time Game Engines/48/en
The agent model is trained using PPO (Schulman et al., 2017), with a simple CNN as the feature network, following Mnih et al. (2015). It is trained on CPU using the Stable Baselines 3 infrastructure (Raffin et al., 2021). The agent is provided with downscaled versions of the frame images and in-game map, each at resolution 160x120. The agent also has access to the last 32 actions it performed. The feature network computes a representation of size 512 for each image. PPO’s actor and critic are 2-layer MLP heads on top of a concatenation of the outputs of the image feature network and the sequence of past actions. We train the agent to play the game using the Vizdoom environment (Wydmuch et al., 2019). We run 8 games in parallel, each with a replay buffer size of 512, a discount factor , and an entropy coefficient of . In each iteration, the network is trained using a batch size of 64 for 10 epochs, with a learning rate of 1e-4. We perform a total of 10M environment steps.
