init commit.

source/rsl_rl/config/example_config.yaml

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