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tqc-ReachCube-v0

Reinforcement Learning
stable-baselines3
ReachCube-v0
deep-reinforcement-learning
Eval Results
Model card Files
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Community
qgallouedec HF Staff commited on
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Files changed (11) hide show
  1. README.md +1 -1
  2. replay.mp4 +2 -2
  3. results.json +1 -1
  4. tqc-ReachCube-v0.zip +2 -2
  5. tqc-ReachCube-v0/actor.optimizer.pth +1 -1
  6. tqc-ReachCube-v0/critic.optimizer.pth +1 -1
  7. tqc-ReachCube-v0/data +16 -16
  8. tqc-ReachCube-v0/ent_coef_optimizer.pth +1 -1
  9. tqc-ReachCube-v0/policy.pth +1 -1
  10. tqc-ReachCube-v0/pytorch_variables.pth +1 -1
  11. train_eval_metrics.zip +2 -2
README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
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  type: ReachCube-v0
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  metrics:
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  - type: mean_reward
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- value: -55.90 +/- 19.16
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  name: mean_reward
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  verified: false
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  ---
 
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  type: ReachCube-v0
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  metrics:
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  - type: mean_reward
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  name: mean_reward
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  ---
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  "__doc__": "\n Policy class (with both actor and critic) for TQC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
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  },
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- "_n_updates": 24985,
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@@ -83,12 +83,12 @@
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  "__module__": "stable_baselines3.common.buffers",
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  "__annotations__": "{'observation_space': <class 'gymnasium.spaces.dict.Dict'>, 'obs_shape': typing.Dict[str, typing.Tuple[int, ...]], 'observations': typing.Dict[str, numpy.ndarray], 'next_observations': typing.Dict[str, numpy.ndarray]}",
85
  "__doc__": "\n Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n Extends the ReplayBuffer to use dictionary observations\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
86
- "__init__": "<function DictReplayBuffer.__init__ at 0x7fca09cf63b0>",
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- "add": "<function DictReplayBuffer.add at 0x7fca09cf6440>",
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- "sample": "<function DictReplayBuffer.sample at 0x7fca09cf64d0>",
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- "_get_samples": "<function DictReplayBuffer._get_samples at 0x7fca09cf6560>",
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  "__abstractmethods__": "frozenset()",
91
- "_abc_impl": "<_abc._abc_data object at 0x7fca09cf0e40>"
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  },
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  "replay_buffer_kwargs": {},
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  "train_freq": {
 
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  "__module__": "sb3_contrib.tqc.policies",
6
  "__doc__": "\n Policy class (with both actor and critic) for TQC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
7
+ "__init__": "<function MultiInputPolicy.__init__ at 0x7f3b3e53b7f0>",
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  "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc._abc_data object at 0x7f3b3e53ff80>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {
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  "use_sde": true
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  },
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+ "num_timesteps": 899712,
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  "_total_timesteps": 10000000,
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  "_num_timesteps_at_start": 0,
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  "seed": 0,
 
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  },
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  "_last_original_obs": {
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+ "arm_qvel": "[[-2.4249406 -3.7272623 -1.9955964 -5.7815127 -5.012941 4.986088 ]\n [-0.6200149 -0.6230333 -1.3865778 -6.618409 -4.95689 4.9292474 ]\n [-0.9781596 -1.1209867 1.4226886 3.0587294 -4.8621807 -4.9995203 ]\n [ 1.8853204 -5.0714283 -0.01716512 3.9760475 4.991297 -4.9334025 ]\n [ 2.5464566 1.8606592 8.161201 -7.69925 -4.8677497 -4.6037736 ]\n [-1.7627236 1.9627852 1.1948799 3.3018155 -5.0016947 -4.9956017 ]\n [ 0.5162484 -1.5015898 3.7568562 0.68446636 -4.5360055 -4.833445 ]\n [ 1.132854 -1.1798022 3.155027 -2.9605818 4.7163734 -4.6850567 ]\n [-2.6996274 2.8439605 2.6669183 -6.3234277 -5.6213317 4.0497265 ]\n [ 1.0563331 -5.264262 2.8138056 4.49152 -4.993588 4.8865447 ]\n [ 0.04417357 1.9560821 -0.21392994 3.9268987 -4.895625 -2.5026567 ]\n [ 3.5431247 1.0761436 -2.6514566 -2.4900074 4.981439 -4.768396 ]\n [-1.0245122 -0.58326054 -2.1507 4.801327 -4.999818 5.0313373 ]\n [-1.8955749 -3.0509884 -2.041924 -5.7639794 -5.5835876 5.0033374 ]\n [ 1.7240446 -1.2754161 -1.4536532 -5.422626 -4.994596 5.0169997 ]\n [ 0.4518194 -2.9691024 -1.6649592 -5.9966354 -5.3844733 5.036947 ]]",
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+ "cube_pos": "[[ 0.0975794 0.03918837 0.01498293]\n [-0.16729686 -0.02646303 0.01498293]\n [ 0.1563035 0.12432317 0.01498293]\n [-0.126937 0.10355998 0.01498269]\n [-0.01893186 0.05619162 0.01498293]\n [ 0.084977 0.10752213 0.01498147]\n [ 0.1413693 0.14569078 0.01453683]\n [-0.56251717 -0.09591984 0.01946527]\n [-0.44930106 0.09230431 0.01498286]\n [-0.23271537 -0.00133253 0.01498293]\n [ 0.16986011 0.07817044 0.01498293]\n [-0.7649278 0.06505291 0.01570172]\n [-0.10477455 0.3409614 0.01498293]\n [-0.04529307 0.03058304 0.01498293]\n [-0.26304722 -0.0242279 0.01417134]\n [-0.042089 -0.00354921 0.01498293]]"
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  },
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+ "_episode_num": 1792,
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  "use_sde": true,
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  "sde_sample_freq": -1,
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+ "_current_progress_remaining": 0.9100304,
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  "_stats_window_size": 100,
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  "ep_info_buffer": {
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  "__doc__": "\n Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n Extends the ReplayBuffer to use dictionary observations\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
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  },
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