.. JAXAtari documentation master file JAXAtari ===================================== JAXAtari is a GPU-accelerated, object-centric Atari environment framework built on `JAX `_, designed for fast and scalable reinforcement learning research. It reimplements classic Atari 2600 games natively in JAX, enabling up to 16,000x faster training speeds through just-in-time (JIT) compilation and massive GPU parallelization,and separates the details of game simulation from agent design. Users can interact with environments through a flexible wrapper system supporting pixel, object-centric, and combined observations. JAXAtari extends the lineage of OCAtari and HackAtari by providing structured, object-level state representations alongside support for parameterized game modifications to test agent generalization. If you use JAXAtari in your research, we ask that you please cite the paper. .. code-block:: python import jax import jaxatari from jaxatari.wrappers import AtariWrapper, ObjectCentricWrapper, LogWrapper # Create an environment env = jaxatari.make("pong") # Apply wrappers for object-centric observations and logging env = LogWrapper(ObjectCentricWrapper(AtariWrapper(env))) # Initialize the environment rng = jax.random.PRNGKey(42) obs, state = env.reset(rng) for _ in range(1000): rng, rng_act = jax.random.split(rng) # This is where you would insert your policy action = jax.random.randint(rng_act, (), 0, env.action_space().n) # Step through the environment # receiving the next observation, reward, done flag and info obs, state, reward, done, info = env.step(state, action) # If the episode has ended, reset to start a new one if done: obs, state = env.reset(rng) .. toctree:: :maxdepth: 1 :caption: Introduction :hidden: introduction/installation introduction/usage introduction/environment introduction/Environments/index .. toctree:: :maxdepth: 2 :caption: API :hidden: api/core api/wrappers api/spaces api/rendering