default max episode length now infinite + release 0.5

rtgym/envs/real_time_env.py

@@ -16,6 +16,7 @@
 from collections import deque
 from threading import Thread, Lock
 import warnings
+import numpy as np
 
 
 # General Interface class ==============================================================================================
@@ -132,7 +133,7 @@ def render(self):
     # you need to perform observation capture in a parallel process and simply retrieve the last available observation
     # in the get_obs_rew_done_info() and reset() methods of your interface
     "time_step_timeout_factor": 1.0,  # maximum elasticity in (fraction or number of) time-steps
-    "ep_max_length": 1000,  # maximum episode length
+    "ep_max_length": np.inf,  # maximum episode length
     "real_time": True,  # True unless you want to revert to the usual turn-based RL setting (not tested yet)
     "async_threading": True,  # True unless you want to revert to the usual turn-based RL setting (not tested yet)
     "act_in_obs": True,  # When True, the action buffer will be appended to observations

setup.py

@@ -7,14 +7,14 @@
 
 setup(name='rtgym',
       packages=[package for package in find_packages()],
-      version='0.4',
+      version='0.5',
       license='MIT',
       description='Easily implement custom OpenAI Gym environments for real-time applications',
       long_description=long_description,
       long_description_content_type="text/markdown",
       author='Yann Bouteiller',
       url='https://github.com/yannbouteiller/rtgym',
-      download_url='https://github.com/yannbouteiller/rtgym/archive/refs/tags/v0.4.tar.gz',
+      download_url='https://github.com/yannbouteiller/rtgym/archive/refs/tags/v0.5.tar.gz',
       keywords=['gym', 'real', 'time', 'custom', 'environment', 'reinforcement', 'learning', 'random', 'delays'],
       install_requires=['gym', 'numpy'],
       classifiers=[