49 lines
1.4 KiB
YAML
49 lines
1.4 KiB
YAML
algorithm:
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class_name: PPO
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# training parameters
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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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# -- 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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policy:
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class_name: ActorCritic
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# for MLP i.e. `ActorCritic`
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activation: elu
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actor_hidden_dims: [128, 128, 128]
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critic_hidden_dims: [128, 128, 128]
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init_noise_std: 1.0
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# only needed for `ActorCriticRecurrent`
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# rnn_type: 'lstm'
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# rnn_hidden_size: 512
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# rnn_num_layers: 1
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runner:
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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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empirical_normalization: false
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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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# -- load and resuming
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resume: false
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load_run: -1 # -1 means load latest run
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resume_path: null # updated from load_run and checkpoint
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checkpoint: -1 # -1 means load latest checkpoint
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runner_class_name: OnPolicyRunner
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seed: 1
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