added .pck file

rllib_config.yaml

@@ -1,60 +0,0 @@
-algorithm: PPO
-
-# Multi-agent-env setting:
-# If true:
-# - Any AIController with done = true will receive zeroes as action values until all AIControllers are done, an episode ends at that point.
-# - ai_controller.needs_reset will also be set to true every time a new episode begins (but you can ignore it in your env if needed).
-# If false:
-# - AIControllers auto-reset in Godot and will receive actions after setting done = true.
-# - Each AIController has its own episodes that can end/reset at any point.
-# Set to false if you have a single policy name for all agents set in AIControllers
-env_is_multiagent: false
-
-checkpoint_frequency: 20
-
-# You can set one or more stopping criteria
-stop:
-    #episode_reward_mean: 0
-    #training_iteration: 1000
-    #timesteps_total: 10000
-    time_total_s: 10000000
-
-config:
-    env: godot
-    env_config:
-        env_path: '/home/valapeos/Projects/pneumarl/pneuma.x86_64' # Set your env path here (exported executable from Godot) - e.g. env_path: 'env_path.exe' on Windows
-        action_repeat: null # Doesn't need to be set here, you can set this in sync node in Godot editor as well
-        show_window: true # Displays game window while training. Might be faster when false in some cases, turning off also reduces GPU usage if you don't need rendering.
-        speedup: 30 # Speeds up Godot physics
-
-    framework: torch # ONNX models exported with torch are compatible with the current Godot RL Agents Plugin
-
-    lr: 0.0003
-    lambda: 0.95
-    gamma: 0.99
-
-    vf_loss_coeff: 0.5
-    vf_clip_param: .inf
-    #clip_param: 0.2
-    entropy_coeff: 0.0001
-    entropy_coeff_schedule: null
-    #grad_clip: 0.5
-
-    normalize_actions: False
-    clip_actions: True # During onnx inference we simply clip the actions to [-1.0, 1.0] range, set here to match
-
-    rollout_fragment_length: 32
-    sgd_minibatch_size: 128
-    num_workers: 4
-    num_envs_per_worker: 1 # This will be set automatically if not multi-agent. If multi-agent, changing this changes how many envs to launch per worker.
-    # The value below needs changing per env
-    # Basic calculation for this value can be rollout_fragment_length * num_workers * num_envs_per_worker (how many AIControllers you have if not multi_agent, otherwise the value you set)
-    train_batch_size: 2048 
-
-    num_sgd_iter: 4
-    batch_mode: truncate_episodes
-
-    num_gpus: 0
-    model:
-        vf_share_layers: False
-        fcnet_hiddens: [64, 64]