Update configuration files for improved performance and flexibility
- Increased frame rate in `maniskill_example.yaml` from 20 to 400 for enhanced simulation speed. - Updated `sac_maniskill.yaml` to set `dataset_repo_id` to null and adjusted `grad_clip_norm` from 10.0 to 40.0. - Changed `storage_device` from "cpu" to "cuda" for better resource utilization. - Modified `save_freq` from 2000000 to 1000000 to optimize saving intervals. - Enhanced input normalization parameters for `observation.state` and `observation.image` in SAC policy. - Adjusted `num_critics` from 10 to 2 and `policy_parameters_push_frequency` from 1 to 4 for improved training dynamics. - Updated `learner_server.py` to utilize `offline_buffer_capacity` for replay buffer initialization. - Changed action multiplier in `maniskill_manipulator.py` from 1 to 0.03 for finer control over actions.
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AdilZouitine
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@ -1,6 +1,6 @@
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# @package _global_
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fps: 20
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fps: 400
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env:
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name: maniskill/pushcube
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@ -8,22 +8,23 @@
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# env.gym.obs_type=environment_state_agent_pos \
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seed: 1
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dataset_repo_id: "AdilZtn/Maniskill-Pushcube-demonstration-medium"
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# dataset_repo_id: "AdilZtn/Maniskill-Pushcube-demonstration-medium"
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dataset_repo_id: null
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training:
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# Offline training dataloader
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num_workers: 4
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batch_size: 512
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grad_clip_norm: 10.0
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grad_clip_norm: 40.0
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lr: 3e-4
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storage_device: "cpu"
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storage_device: "cuda"
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eval_freq: 2500
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log_freq: 10
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save_freq: 2000000
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save_freq: 1000000
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online_steps: 1000000
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online_rollout_n_episodes: 10
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@ -32,17 +33,12 @@ training:
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online_sampling_ratio: 1.0
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online_env_seed: 10000
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online_buffer_capacity: 200000
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offline_buffer_capacity: 100000
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online_buffer_seed_size: 0
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online_step_before_learning: 500
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do_online_rollout_async: false
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policy_update_freq: 1
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# delta_timestamps:
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# observation.environment_state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
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# observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
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# action: "[i / ${fps} for i in range(${policy.horizon})]"
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# next.reward: "[i / ${fps} for i in range(${policy.horizon})]"
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policy:
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name: sac
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@ -68,28 +64,33 @@ policy:
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camera_number: 1
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# Normalization / Unnormalization
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input_normalization_modes: null
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# input_normalization_modes:
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# observation.state: min_max
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input_normalization_params: null
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# observation.state:
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# min: [-1.9361e+00, -7.7640e-01, -7.7094e-01, -2.9709e+00, -8.5656e-01,
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# 1.0764e+00, -1.2680e+00, 0.0000e+00, 0.0000e+00, -9.3448e+00,
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# -3.3828e+00, -3.8420e+00, -5.2553e+00, -3.4154e+00, -6.5082e+00,
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# -6.0500e+00, -8.7193e+00, -8.2337e+00, -3.4650e-01, -4.9441e-01,
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# 8.3516e-03, -3.1114e-01, -9.9700e-01, -2.3471e-01, -2.7137e-01]
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# input_normalization_modes: null
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input_normalization_modes:
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observation.state: min_max
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observation.image: mean_std
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# input_normalization_params: null
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input_normalization_params:
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observation.state:
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min: [-1.9361e+00, -7.7640e-01, -7.7094e-01, -2.9709e+00, -8.5656e-01,
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1.0764e+00, -1.2680e+00, 0.0000e+00, 0.0000e+00, -9.3448e+00,
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-3.3828e+00, -3.8420e+00, -5.2553e+00, -3.4154e+00, -6.5082e+00,
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-6.0500e+00, -8.7193e+00, -8.2337e+00, -3.4650e-01, -4.9441e-01,
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8.3516e-03, -3.1114e-01, -9.9700e-01, -2.3471e-01, -2.7137e-01]
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max: [ 0.8644, 1.4306, 1.8520, -0.7578, 0.9508, 3.4901, 1.9381, 0.0400,
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0.0400, 5.0885, 4.7156, 7.9393, 7.9100, 2.9796, 5.7720, 4.7163,
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7.8145, 9.7415, 0.2422, 0.4505, 0.6306, 0.2622, 1.0000, 0.5135,
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0.4001]
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# max: [ 0.8644, 1.4306, 1.8520, -0.7578, 0.9508, 3.4901, 1.9381, 0.0400,
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# 0.0400, 5.0885, 4.7156, 7.9393, 7.9100, 2.9796, 5.7720, 4.7163,
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# 7.8145, 9.7415, 0.2422, 0.4505, 0.6306, 0.2622, 1.0000, 0.5135,
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# 0.4001]
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observation.image:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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output_normalization_modes:
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action: min_max
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output_normalization_params:
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action:
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min: [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0]
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max: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
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min: [-0.03, -0.03, -0.03, -0.03, -0.03, -0.03, -0.03]
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max: [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]
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output_normalization_shapes:
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action: [7]
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@ -99,8 +100,8 @@ policy:
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# discount: 0.99
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discount: 0.80
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temperature_init: 1.0
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num_critics: 10 #10
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num_subsample_critics: 2
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num_critics: 2 #10
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num_subsample_critics: null
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critic_lr: 3e-4
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actor_lr: 3e-4
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temperature_lr: 3e-4
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@ -111,7 +112,7 @@ policy:
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actor_learner_config:
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learner_host: "127.0.0.1"
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learner_port: 50051
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policy_parameters_push_frequency: 1
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policy_parameters_push_frequency: 4
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concurrency:
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actor: 'processes'
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learner: 'processes'
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actor: 'threads'
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learner: 'threads'
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@ -202,6 +202,7 @@ def initialize_offline_replay_buffer(
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action_delta=cfg.env.wrapper.delta_action,
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storage_device=storage_device,
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optimize_memory=True,
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capacity=cfg.training.offline_buffer_capacity,
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)
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return offline_replay_buffer
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@ -159,7 +159,7 @@ def make_maniskill(
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env.unwrapped.metadata["render_fps"] = 20
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env = ManiSkillCompat(env)
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env = ManiSkillActionWrapper(env)
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env = ManiSkillMultiplyActionWrapper(env, multiply_factor=1)
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env = ManiSkillMultiplyActionWrapper(env, multiply_factor=0.03)
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return env
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