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docs/source/environment.rst

@@ -7,4 +7,4 @@ RL environments are the objects that directly interface with RL libraries such a
 * Ray Single agent API - For training a single Ray RLLib agent
 * Ray MARL API - For training multi-agent systems with Ray RLLib. ``PrimaiteRayMARLEnv`` adheres to the `Official Ray documentation <https://docs.ray.io/en/latest/rllib/package_ref/env/multi_agent_env.html>`_.
 
-There is a Jupyter notebook which demonstrates integration with each of these three environments. They are located in ``~/primaite/<VERSION>/notebooks/example_notebooks``.
+There are Jupyter notebooks which demonstrate integration with each of these three environments. They are located in ``~/primaite/<VERSION>/notebooks/example_notebooks``.

src/primaite/config/_package_data/example_config_2_rl_agents.yaml

@@ -1,11 +1,17 @@
 training_config:
   rl_framework: RLLIB_multi_agent
-  # rl_framework: SB3
+  rl_algorithm: PPO
+  seed: 333
+  n_learn_episodes: 1
+  n_eval_episodes: 5
+  max_steps_per_episode: 256
+  deterministic_eval: false
   n_agents: 2
   agent_references:
     - defender_1
     - defender_2
 
+
 io_settings:
   save_checkpoints: true
   checkpoint_interval: 5