78 lines
2.9 KiB
YAML
78 lines
2.9 KiB
YAML
runner:
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class_name: OnPolicyRunner
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# General
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num_steps_per_env: 24 # Number of steps per environment per iteration
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max_iterations: 1500 # Number of policy updates
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seed: 1
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# Observations
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obs_groups: {"policy": ["policy"], "critic": ["policy", "privileged"]} # Maps observation groups to sets. See `vec_env.py` for more information
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# Logging parameters
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save_interval: 50 # Check for potential saves every `save_interval` iterations
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experiment_name: walking_experiment
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run_name: ""
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# Logging writer
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logger: tensorboard # tensorboard, neptune, wandb
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neptune_project: legged_gym
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wandb_project: legged_gym
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# Policy
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policy:
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class_name: ActorCritic
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activation: elu
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actor_obs_normalization: false
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critic_obs_normalization: false
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actor_hidden_dims: [256, 256, 256]
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critic_hidden_dims: [256, 256, 256]
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init_noise_std: 1.0
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noise_std_type: "scalar" # 'scalar' or 'log'
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state_dependent_std: false
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# Algorithm
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algorithm:
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class_name: PPO
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# Training
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learning_rate: 0.001
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num_learning_epochs: 5
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num_mini_batches: 4 # mini batch size = num_envs * num_steps / num_mini_batches
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schedule: adaptive # adaptive, fixed
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# Value function
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value_loss_coef: 1.0
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clip_param: 0.2
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use_clipped_value_loss: true
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# Surrogate loss
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desired_kl: 0.01
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entropy_coef: 0.01
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gamma: 0.99
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lam: 0.95
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max_grad_norm: 1.0
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# Miscellaneous
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normalize_advantage_per_mini_batch: false
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# Random network distillation
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rnd_cfg:
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weight: 0.0 # Initial weight of the RND reward
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weight_schedule: null # This is a dictionary with a required key called "mode". Please check the RND module for more information
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reward_normalization: false # Whether to normalize RND reward
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# Learning parameters
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learning_rate: 0.001 # Learning rate for RND
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# Network parameters
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num_outputs: 1 # Number of outputs of RND network. Note: if -1, then the network will use dimensions of the observation
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predictor_hidden_dims: [-1] # Hidden dimensions of predictor network
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target_hidden_dims: [-1] # Hidden dimensions of target network
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# Symmetry augmentation
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symmetry_cfg:
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use_data_augmentation: true # This adds symmetric trajectories to the batch
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use_mirror_loss: false # This adds symmetry loss term to the loss function
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data_augmentation_func: null # String containing the module and function name to import
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# Example: "legged_gym.envs.locomotion.anymal_c.symmetry:get_symmetric_states"
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#
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# .. code-block:: python
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#
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# @torch.no_grad()
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# def get_symmetric_states(
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# obs: Optional[torch.Tensor] = None, actions: Optional[torch.Tensor] = None, cfg: "BaseEnvCfg" = None, obs_type: str = "policy"
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# ) -> Tuple[torch.Tensor, torch.Tensor]:
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#
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mirror_loss_coeff: 0.0 # Coefficient for symmetry loss term. If 0, no symmetry loss is used
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