more timesteps1wv9sri6

1wv9sri6.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:722eddb2084a13374f34b70ad6479749e64bce846c4028898f563a4a73dc4f7d
+size 144758

1wv9sri6/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

1wv9sri6/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7f85a1618d30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f85a1618dc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f85a1618e50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f85a1618ee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f85a1618f70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f85a161c040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f85a161c0d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f85a161c160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f85a161c1f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f85a161c280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f85a161c310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f85a160f9c0>"
+    },
+    "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": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 32,
+    "num_timesteps": 3014656,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652014080.4665875,
+    "learning_rate": 0.0001,
+    "tensorboard_log": "runs/1wv9sri6",
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQQAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYABAAAAAAAAEBaHz4a5Jo/kW0CPt5M+b5ROKE+z8eRvAAAAAAAAAAARtJMvp8afj93o4i+YwEQvysrib6LyPe9AAAAAAAAAABA8OI9UrD/uTGcLjMHzNQqwAvCOqKE0rMAAIA/AACAPzNlCrx73p26S7wbNpeoHjFpxIO4/v8/tQAAgD8AAIA/uxOgvtCaOD+ZoLc+hQfrvsvbXr767mA+AAAAAAAAAADN3WA9T/IDvPAfcL3PTiA9ZAVwPbrFAr4AAIA/AACAP830vTzaVhY/0mygvfwryb4YyqE6F9cDvQAAAAAAAAAAALmrPM8UDbwHSQY8kZGOPDJtaz098W29AACAPwAAgD+aIz+8NxRkPrnTlD3fLau+WbLVvKsYSDwAAAAAAAAAAOZv+L0lm2Y+7RXKPh/Lm76CgFA9ckn5PQAAAAAAAAAApqmTvcNpVrpCqN64OYxitOj+cDqGlQA4AAAAAAAAAAB9Coi+8DdLPz/oKb7VK/m+ddrHvvBUOD0AAAAAAAAAAOaBej0Y+fI+kQ4Cvybm8r5usIq+UtEvvgAAAAAAAAAA5sW7PQH2DT/pesG9KizEvjQlET2dcIe9AAAAAAAAAABNh3C9e+aeukRiyTrBfYm1nPTMurP6dbQAAAAAAACAP5o51rsUhIC60/XUNvcHxzDeoL26I1H5tQAAgD8AAIA/ABBNuxlooz9/rTe9u+ILv/NwyTyWGoo9AAAAAAAAAAAAgEI5KRpVPhGjSD2Fp6C+ToQLvLKRyr0AAAAAAAAAAJr8k71a7ak/nbG4vvu/3b654ai9omBxvgAAAAAAAAAAzQa5PkkslD9qF4E+UUgEv2y2Jz+p4ga9AAAAAAAAAABAEwQ+pR0IP7wQF76/Vs++aqvxPODN0b0AAAAAAAAAAFrrFL55JZY+x32ePhNstb4qPqK8OvPGPQAAAAAAAAAAptidvXvm2rpUK6C8J2GFPIF7RzvqG2i9AACAPwAAgD+a2fI5VIKTvFjsvjselhk8THQFvizNAD0AAIA/AACAPzPlWLwJhRc9R/AbPgEPiL5FSpk9nuB8vQAAAAAAAAAAGmoyPR89xrvYdMo6EHSxPHf6Nz19iJS9AACAPwAAgD8a0SW9RQKHPDqnsD3mFpq+huWSvTIJSD0AAAAAAAAAAJotGT6YjAM/VS8xvq3oxb7GcBs9hBqvvQAAAAAAAAAAhm02vmrfJD8HkQa9H6zivgWTM76A2OM9AAAAAAAAAAAAPEk84by+upYyLjxkuVY1oMd7ufc5RTQAAIA/AACAP7OrGb2fmv48hLhBPsODlr4XHho9TmgAPQAAAAAAAAAABvUoPn2X1D5QQjy+r1PCvmLJ3ztiktm9AAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSyBLCIaUjAFDlHSUUpQu"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 690,
+    "n_steps": 2048,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 256,
+    "n_epochs": 15,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

1wv9sri6/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7794988fb793dca280f24cbb43bd3f22598d2b2024f1a26a81250e0d47a67505
+size 84893

1wv9sri6/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8c7a063adac3f5f1a31d87301489d2820444d2f27efde0c50b885b2cba1efeb
+size 43201

1wv9sri6/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

1wv9sri6/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.0-97-generic-x86_64-with-glibc2.10 #110~18.04.1-Ubuntu SMP Mon Jan 17 20:50:31 UTC 2022
+Python: 3.8.12
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0a0+17540c5
+GPU Enabled: True
+Numpy: 1.22.2
+Gym: 0.21.0

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 248.39 +/- 20.18
+      value: 284.55 +/- 22.27
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7fc3dbf7ec20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc3dbf7ecb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc3dbf7ed40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc3dbf7edd0>", "_build": "<function ActorCriticPolicy._build at 0x7fc3dbf7ee60>", "forward": "<function ActorCriticPolicy.forward at 0x7fc3dbf7eef0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc3dbf7ef80>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc3dbf81050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc3dbf810e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc3dbf81170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc3dbf81200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc3dbfcba80>"}, "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": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 32, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651938265.6724284, "learning_rate": 0.0001, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 230, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f85a1618d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f85a1618dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f85a1618e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f85a1618ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f85a1618f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f85a161c040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f85a161c0d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f85a161c160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f85a161c1f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f85a161c280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f85a161c310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f85a160f9c0>"}, "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": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 32, "num_timesteps": 3014656, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652014080.4665875, "learning_rate": 0.0001, "tensorboard_log": "runs/1wv9sri6", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 690, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 256, "n_epochs": 15, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.0-97-generic-x86_64-with-glibc2.10 #110~18.04.1-Ubuntu SMP Mon Jan 17 20:50:31 UTC 2022", "Python": "3.8.12", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0a0+17540c5", "GPU Enabled": "True", "Numpy": "1.22.2", "Gym": "0.21.0"}}

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e18c4a63ee6520b3f53ed4a0c877cc2c47e4dc6edb938bb369af397dec966a77
-size 248317
+oid sha256:560fec89c0b03463ed15c21e2b65d5e5f9ef1b87be1543a7a3ddb2a3e1e5f038
+size 231978

results.json

@@ -1 +1 @@
-{"mean_reward": 248.38613631511757, "std_reward": 20.179615247159667, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-07T16:22:53.067683"}
+{"mean_reward": 284.551800296675, "std_reward": 22.26604943503135, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-08T20:57:25.322883"}