Galeros commited on
Commit
81e6c97
1 Parent(s): d7efe22

PPO with 3e6 iterations

Browse files
README.md CHANGED
@@ -10,7 +10,7 @@ model-index:
10
  results:
11
  - metrics:
12
  - type: mean_reward
13
- value: 276.68 +/- 23.99
14
  name: mean_reward
15
  task:
16
  type: reinforcement-learning
 
10
  results:
11
  - metrics:
12
  - type: mean_reward
13
+ value: 281.43 +/- 23.89
14
  name: mean_reward
15
  task:
16
  type: reinforcement-learning
config.json CHANGED
@@ -1 +1 @@
1
- {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7fee74909ea0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fee74909f28>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fee7490f048>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fee7490f0d0>", "_build": "<function ActorCriticPolicy._build at 0x7fee7490f158>", "forward": "<function ActorCriticPolicy.forward at 0x7fee7490f1e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fee7490f268>", "_predict": "<function ActorCriticPolicy._predict at 0x7fee7490f2f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fee7490f378>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fee7490f400>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fee7490f488>", "__abstractmethods__": "frozenset()", "_abc_registry": "<_weakrefset.WeakSet object at 0x7fee74908240>", "_abc_cache": "<_weakrefset.WeakSet object at 0x7fee74908278>", "_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7fee749082b0>", "_abc_negative_cache_version": 59}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": "RandomState(MT19937)"}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "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", "n": 4, "shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 1, "num_timesteps": 3000320, "_total_timesteps": 3000000, "seed": null, "action_noise": null, "start_time": 1653589457.7667656, "learning_rate": 0.0003, "tensorboard_log": "./ppo_lunarlander_v2_tensorboard/", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gASVBQMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxrL2hvbWUvYWdhcmNpYS9yZXBvcy9kZWVwLXJsLWNsYXNzL3VuaXQxL3VuaXQxL2xpYi9weXRob24zLjYvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjGsvaG9tZS9hZ2FyY2lhL3JlcG9zL2RlZXAtcmwtY2xhc3MvdW5pdDEvdW5pdDEvbGliL3B5dGhvbjMuNi9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGggfZR9lChoF2gOjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoGIwHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVqgAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwFLCIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUMgAJWrPKTYC7vC2MM7gqCWPMqxC7xa8IE9AACAPwAAgD+UdJRiLg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASViQAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwGFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDAQCUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00010666666666669933, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 11720, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "target_kl": null, "system_info": {"OS": "Linux-4.15.0-180-generic-x86_64-with-Ubuntu-18.04-bionic #189-Ubuntu SMP Wed May 18 14:13:57 UTC 2022", "Python": "3.6.9", "Stable-Baselines3": "1.3.0", "PyTorch": "1.10.2+cu102", "GPU Enabled": "True", "Numpy": "1.19.5", "Gym": "0.19.0"}}
 
1
+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f05a9379d08>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f05a9379d90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f05a9379e18>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f05a9379ea0>", "_build": "<function ActorCriticPolicy._build at 0x7f05a9379f28>", "forward": "<function ActorCriticPolicy.forward at 0x7f05a937e048>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f05a937e0d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f05a937e158>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f05a937e1e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f05a937e268>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f05a937e2f0>", "__abstractmethods__": "frozenset()", "_abc_registry": "<_weakrefset.WeakSet object at 0x7f05a93edeb8>", "_abc_cache": "<_weakrefset.WeakSet object at 0x7f05a93edef0>", "_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7f05a93edf28>", "_abc_negative_cache_version": 59}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": "RandomState(MT19937)"}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "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", "n": 4, "shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 1, "num_timesteps": 3000320, "_total_timesteps": 3000000, "seed": null, "action_noise": null, "start_time": 1653589457.7667656, "learning_rate": 0.0003, "tensorboard_log": "./ppo_lunarlander_v2_tensorboard/", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVqgAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwFLCIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUMgAJWrPKTYC7vC2MM7gqCWPMqxC7xa8IE9AACAPwAAgD+UdJRiLg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASViQAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwGFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDAQCUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00010666666666669933, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 11720, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "target_kl": null, "system_info": {"OS": "Linux-4.15.0-180-generic-x86_64-with-Ubuntu-18.04-bionic #189-Ubuntu SMP Wed May 18 14:13:57 UTC 2022", "Python": "3.6.9", "Stable-Baselines3": "1.3.0", "PyTorch": "1.10.2+cu102", "GPU Enabled": "True", "Numpy": "1.19.5", "Gym": "0.19.0"}}
galeos_model_lander_ppo.zip CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:dfd48d7a45f20c0359da80e1b47acb8d2b549481cbb45ac6a5a74c061bf658ad
3
  size 151163
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:739882ef90a54cbfb528c27235ed9b951c13d139788795a4f8af4b6c03b4d88e
3
  size 151163
galeos_model_lander_ppo/data CHANGED
@@ -4,21 +4,21 @@
4
  ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
5
  "__module__": "stable_baselines3.common.policies",
6
  "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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 ",
7
- "__init__": "<function ActorCriticPolicy.__init__ at 0x7fee74909ea0>",
8
- "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fee74909f28>",
9
- "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fee7490f048>",
10
- "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fee7490f0d0>",
11
- "_build": "<function ActorCriticPolicy._build at 0x7fee7490f158>",
12
- "forward": "<function ActorCriticPolicy.forward at 0x7fee7490f1e0>",
13
- "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fee7490f268>",
14
- "_predict": "<function ActorCriticPolicy._predict at 0x7fee7490f2f0>",
15
- "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fee7490f378>",
16
- "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fee7490f400>",
17
- "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fee7490f488>",
18
  "__abstractmethods__": "frozenset()",
19
- "_abc_registry": "<_weakrefset.WeakSet object at 0x7fee74908240>",
20
- "_abc_cache": "<_weakrefset.WeakSet object at 0x7fee74908278>",
21
- "_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7fee749082b0>",
22
  "_abc_negative_cache_version": 59
23
  },
24
  "verbose": 1,
 
4
  ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
5
  "__module__": "stable_baselines3.common.policies",
6
  "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7f05a9379d08>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f05a9379d90>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f05a9379e18>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f05a9379ea0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f05a9379f28>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f05a937e048>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f05a937e0d0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f05a937e158>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f05a937e1e0>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f05a937e268>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f05a937e2f0>",
18
  "__abstractmethods__": "frozenset()",
19
+ "_abc_registry": "<_weakrefset.WeakSet object at 0x7f05a93edeb8>",
20
+ "_abc_cache": "<_weakrefset.WeakSet object at 0x7f05a93edef0>",
21
+ "_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7f05a93edf28>",
22
  "_abc_negative_cache_version": 59
23
  },
24
  "verbose": 1,
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": 276.6844665838818, "std_reward": 23.994863457176383, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-29T21:25:44.457497"}
 
1
+ {"mean_reward": 281.43437720458627, "std_reward": 23.892504554624935, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-29T21:31:39.071226"}