The time perceived by the game can be adjusted with the methods t_speed(double factor) and n_afap(double fps). Then the back buffer is copied into a shared memory space in order to be returned by GameInterface.step. The this releases the lock until wglSwapBuffers is called again. If one wants to advance the game for one step and retrieve the next frame GameInterface.step can be called from python. The wglSwapBuffers hook first locks the games execution. The function wglSwapBuffers is called every time the game finishes rendering a frame in order to swap the back and front buffer. This DLL hooks into the OpenGL function wglSwapBuffers as well as the system calls timeGetTime, GetTickCount, GetTickCount64, and RtlQueryPerformanceCounter. To do so, the library injects a DLL into the game's process. Secondly, it intercepts the system calls used to get the system time such that the game can be run at a desired speed. This library serves two functions:įirstly, it efficiently retrieves the frames and sends them to the python process. In order to efficiently train the agent, a C++ library was written. However, the noise is turned off after 500,000 training iterations.Noisy networks facilitate the exploration process.Distributional RL with quantile regression gives similar results.The distributional approach significantly increases the performance of the agent.However, after roughly 300,000 training steps the agent trained without prioritized experience replay performs better Prioritized experience replay at first performs better,.n-step significantly decreased the performance.Double Q-Learning and Dueling Networks did not improve the performance.All six Rainbow extensions have been evaluated.The used hyperparameters can be found at the bottom of trainer.py below if _name_ = '_main_':.See superhexagon.SuperHexagonInterface._preprocess_frame for more implementational details.Such that the walls and the player belong to the foreground and everything else belongs to the background Additionally, a threshold function is applied to the frame.See utils.Network for more implementational details.For the fully connected part of the network the feature maps of both streams are flattened and concatenated.Since the small triangle which is controlled by the player is barely visible for the first stream,Ī zoomed-in input is given to the second stream.For the first stream the frame is cropped to a square and resized to 60 by 60 pixel.The network has two convolutional streams.The reinforcement learning agent receives a reward of -1 if it dies otherwise a reward of 0.
#Super hexagon ending update
So if all of this sounds good to you, you can purchase the newly-updated Android version of Super Hexagon through the Play Store widget below for $2.99 (this is a premium release), and if you already own the title, simply update it. The game does indeed fit the entire screen of differing ratios (no more pillarboxing), where the 20.4:9 ratio of the ROG 5 displays the game in full. I can also confirm physical controllers work as expected. I can confirm the new high framerate support works well, hitting a stable 144FPS on my ROG 5. Of course, the standout change is the new Android 11 and 12 support, which means the game will now run on your Pixel phone, along with the majority of modern Android hardware.
#Super hexagon ending for android
0 kommentar(er)
