Xitari is a fork of the Arcade Learning Environment v0.4.
=== Original Readme.txt from ALE 0.4, with tidy up by Marc G. Bellemare ===
This is the 0.4 release of the Arcade Learning Environment (ALE), a platform designed for AI research. ALE is based on Stella, an Atari 2600 VCS emulator. More information and ALE-related publications can be found at
http://www.arcadelearningenvironment.org
We encourage you to use the Arcade Learning Environment in your research. In return, we would appreciate if you cited ALE in publications that rely on it (BibTeX entry at the end of this document).
Feedback and suggestions are welcome and may be addressed to any active member of the ALE team.
Enjoy, The ALE team
Execute ./ale -help for more details; alternatively, see documentation available at http://www.arcadelearningenvironment.org.
-random_seed [n] -- sets the random seed; defaults to the current time
-game_controller [fifo|fifo_named|internal] -- specifies how agents interact with ALE; see Java agent documentation for details
-config [file] -- specifies a configuration file, from which additional parameters are read
-output_file [file] -- if set, standard output is redirected to the given file. Do not use in conjunction with -game_controller fifo_named
-run_length_encoding [false|true] -- determine whether run-length encoding is used to send data over pipes; irrelevant when -game_controller internal is set
-max_num_frames_per_episode [n] -- sets the maximum number of frames per episode. Once this number is reached, a new episode will start. Currently implemented on a per-agent basis with internal agents, or for all agents when using pipes (fifo/fifo_named)
These parameters are only relevant when using one of the sample agents under src/agents.
-max_num_episodes [n] -- sets the maximum number of episodes
-max_num_frames [n] -- sets the maximum number of frames (independent of how many episodes are played)