60 lines
2.4 KiB
YAML
60 lines
2.4 KiB
YAML
algorithm: PPO
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# Multi-agent-env setting:
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# If true:
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# - Any AIController with done = true will receive zeroes as action values until all AIControllers are done, an episode ends at that point.
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# - 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).
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# If false:
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# - AIControllers auto-reset in Godot and will receive actions after setting done = true.
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# - Each AIController has its own episodes that can end/reset at any point.
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# Set to false if you have a single policy name for all agents set in AIControllers
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env_is_multiagent: false
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checkpoint_frequency: 20
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# You can set one or more stopping criteria
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stop:
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#episode_reward_mean: 0
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#training_iteration: 1000
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#timesteps_total: 10000
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time_total_s: 10000000
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config:
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env: godot
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env_config:
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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
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action_repeat: null # Doesn't need to be set here, you can set this in sync node in Godot editor as well
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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.
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speedup: 30 # Speeds up Godot physics
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framework: torch # ONNX models exported with torch are compatible with the current Godot RL Agents Plugin
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lr: 0.0003
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lambda: 0.95
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gamma: 0.99
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vf_loss_coeff: 0.5
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vf_clip_param: .inf
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#clip_param: 0.2
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entropy_coeff: 0.0001
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entropy_coeff_schedule: null
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#grad_clip: 0.5
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normalize_actions: False
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clip_actions: True # During onnx inference we simply clip the actions to [-1.0, 1.0] range, set here to match
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rollout_fragment_length: 32
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sgd_minibatch_size: 128
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num_workers: 4
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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.
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# The value below needs changing per env
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# 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)
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train_batch_size: 2048
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num_sgd_iter: 4
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batch_mode: truncate_episodes
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num_gpus: 0
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model:
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vf_share_layers: False
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fcnet_hiddens: [64, 64]
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