2m frames

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 281.43 +/- 20.00
+      value: 280.04 +/- 18.85
       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 0x7f51f4d3bca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f51f4d3bd30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f51f4d3bdc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f51f4d3be50>", "_build": "<function ActorCriticPolicy._build at 0x7f51f4d3bee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f51f4d3bf70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f51f4d40040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f51f4d400d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f51f4d40160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f51f4d401f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f51f4d40280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f51f4d3dc40>"}, "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": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651872647.258572, "learning_rate": 0.0003, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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": 32, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.11.0-38-generic-x86_64-with-glibc2.31 #42~20.04.1-Ubuntu SMP Tue Sep 28 20:41:07 UTC 2021", "Python": "3.9.12", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu102", "GPU Enabled": "True", "Numpy": "1.22.3", "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 0x7f7881f0a3b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7881f0a440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7881f0a4d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7881f0a560>", "_build": "<function ActorCriticPolicy._build at 0x7f7881f0a5f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f7881f0a680>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7881f0a710>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7881f0a7a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7881f0a830>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7881f0a8c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7881f0a950>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7881f49cf0>"}, "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": 16, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651873191.0180867, "learning_rate": 0.0003, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.007616000000000067, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 492, "n_steps": 1024, "gamma": 0.99, "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, "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"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6d06a0280925eee53f3afef31669b900df06a00fc5e096bf53e616f15186ed79
-size 144217
+oid sha256:ee02697c9acd5945acb4e1dfb6057f9aa22bc0d1b960bbc463046598e43614a0
+size 144009

ppo-LunarLander-v2/data

@@ -4,19 +4,19 @@
         ":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 0x7f51f4d3bca0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f51f4d3bd30>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f51f4d3bdc0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f51f4d3be50>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f51f4d3bee0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f51f4d3bf70>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f51f4d40040>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f51f4d400d0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f51f4d40160>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f51f4d401f0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f51f4d40280>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f7881f0a3b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7881f0a440>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7881f0a4d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7881f0a560>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7881f0a5f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7881f0a680>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7881f0a710>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7881f0a7a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7881f0a830>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7881f0a8c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7881f0a950>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f51f4d3dc40>"
+        "_abc_impl": "<_abc_data object at 0x7f7881f49cf0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,21 +42,21 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 1015808,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 2015232,
+    "_total_timesteps": 2000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1651872647.258572,
+    "start_time": 1651873191.0180867,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -66,27 +66,27 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": -0.007616000000000067,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 248,
+    "_n_updates": 492,
     "n_steps": 1024,
-    "gamma": 0.999,
+    "gamma": 0.99,
     "gae_lambda": 0.98,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 32,
+    "batch_size": 64,
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
-        ":serialized:": "gAWV9wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjGMvaG9tZS9ubS9hbmFjb25kYTMvZW52cy9kZWVwLXJsLWNsYXNzL2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjGMvaG9tZS9ubS9hbmFjb25kYTMvZW52cy9kZWVwLXJsLWNsYXNzL2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoIH2UfZQoaBdoDowMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBiMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+        ":serialized:": "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"
     },
     "clip_range_vf": null,
     "normalize_advantage": true,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a36221e13a1515f1025651158c43ede2f041feb93fb4538d6a9034ab7be679c1
+oid sha256:9ec1efb95614a06c431b1fe046f3e18f6747ad906fd52674974ba49ffd3a8a50
 size 84893

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1d6bb0a978b8403c2d18f1baa619d8e18287153e2b8dc0fc3c4aa2872b39f299
+oid sha256:3dfd6d5c5e81ff0fe65ff2a4322dd0837f5abce42e2e9140f4477d5ffab5ac98
 size 43201

ppo-LunarLander-v2/system_info.txt

@@ -1,7 +1,7 @@
-OS: Linux-5.11.0-38-generic-x86_64-with-glibc2.31 #42~20.04.1-Ubuntu SMP Tue Sep 28 20:41:07 UTC 2021
-Python: 3.9.12
+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+cu102
+PyTorch: 1.11.0+cu113
 GPU Enabled: True
-Numpy: 1.22.3
+Numpy: 1.21.6
 Gym: 0.21.0

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9c13ae3c0f6beeb9921f473da6e243188c7af83c019d5446147ee86be508cd50
-size 208195
+oid sha256:d870f480eb392810855b269cbf09fe10434e4d8ac331a0a60ff41988839171ac
+size 217069

results.json

@@ -1 +1 @@
-{"mean_reward": 281.4280464551134, "std_reward": 19.995811029813517, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T23:45:48.312000"}
+{"mean_reward": 280.0433239325761, "std_reward": 18.847070735785138, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T22:20:33.984304"}