FeuerEngel commited on
Commit
abb0364
1 Parent(s): 0bab9be

Upload PPO LunarLander-v2 trained agent with 1mln steps

Browse files
README.md CHANGED
@@ -10,7 +10,7 @@ model-index:
10
  results:
11
  - metrics:
12
  - type: mean_reward
13
- value: -80.59 +/- 42.60
14
  name: mean_reward
15
  task:
16
  type: reinforcement-learning
 
10
  results:
11
  - metrics:
12
  - type: mean_reward
13
+ value: 252.42 +/- 24.34
14
  name: mean_reward
15
  task:
16
  type: reinforcement-learning
config.json CHANGED
@@ -1 +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 0x7f0aa0cf0d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0aa0cf0dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0aa0cf0e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0aa0cf0ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f0aa0cf0f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f0aa0cf3040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0aa0cf30d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0aa0cf3160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0aa0cf31f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0aa0cf3280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0aa0cf3310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0aa0ce5ba0>"}, "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": 0, "_total_timesteps": 0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": null, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": null, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 1, "ep_info_buffer": null, "ep_success_buffer": null, "_n_updates": 0, "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, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.17.5-arch1-1-x86_64-with-glibc2.17 #1 SMP PREEMPT Wed, 27 Apr 2022 20:56:11 +0000", "Python": "3.8.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.21.5", "Gym": "0.21.0"}}
 
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 0x7f0aa0cf0d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0aa0cf0dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0aa0cf0e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0aa0cf0ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f0aa0cf0f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f0aa0cf3040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0aa0cf30d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0aa0cf3160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0aa0cf31f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0aa0cf3280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0aa0cf3310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0aa0ce5ba0>"}, "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": 1652041112.9235907, "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:": "gAWVdxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIz2bV52o+cUCUhpRSlIwBbJRNIwGMAXSUR0CTar2IwdsBdX2UKGgGaAloD0MIcm2oGCdVc0CUhpRSlGgVTTwBaBZHQJNrtrdnCfp1fZQoaAZoCWgPQwjUgaynVjZwQJSGlFKUaBVL8WgWR0CTa++hoM8YdX2UKGgGaAloD0MIV+vE5XhZOkCUhpRSlGgVS8VoFkdAk2yxKDkELnV9lChoBmgJaA9DCGxaKQRyOG9AlIaUUpRoFU1fAWgWR0CTbZCVrylOdX2UKGgGaAloD0MInFJeK2EmckCUhpRSlGgVTRIBaBZHQJNtvMSsbNt1fZQoaAZoCWgPQwgmAWpq2QpvQJSGlFKUaBVNDgFoFkdAk29HQD3dsXV9lChoBmgJaA9DCCAkC5jAb25AlIaUUpRoFU0SAWgWR0CTb5ho/RmcdX2UKGgGaAloD0MIBkmfVhFccECUhpRSlGgVTUsBaBZHQJNv7A44p+d1fZQoaAZoCWgPQwgKE0azMtpxQJSGlFKUaBVNAgFoFkdAk3BjrJKaonV9lChoBmgJaA9DCC9OfLUj729AlIaUUpRoFUvtaBZHQJNwY6eXiR51fZQoaAZoCWgPQwjGbworFahwQJSGlFKUaBVNLgFoFkdAk3CpTyauwHV9lChoBmgJaA9DCOblsPvO23JAlIaUUpRoFU1zAWgWR0CTcUx0MgEEdX2UKGgGaAloD0MI+vIC7COAcUCUhpRSlGgVTXABaBZHQJNxkXHim2t1fZQoaAZoCWgPQwjLnZlgOGBxQJSGlFKUaBVNEAFoFkdAk3H3lbNbDHV9lChoBmgJaA9DCNgubTisW3JAlIaUUpRoFU0kAWgWR0CTclEVWS2ZdX2UKGgGaAloD0MIcm2oGGc8b0CUhpRSlGgVTRUBaBZHQJNztGRV6u51fZQoaAZoCWgPQwgk0jb+xIdxQJSGlFKUaBVL+2gWR0CTc8dOZb6hdX2UKGgGaAloD0MIIEJcOXszbECUhpRSlGgVTSUBaBZHQJNz73YcvM91fZQoaAZoCWgPQwifVWZKa7FvQJSGlFKUaBVNXAFoFkdAk3RimQ8wH3V9lChoBmgJaA9DCBk4oKWr03BAlIaUUpRoFU0RAWgWR0CTdVVObiIddX2UKGgGaAloD0MI7PoFuyHKcUCUhpRSlGgVTTIBaBZHQJN2HRhMJyB1fZQoaAZoCWgPQwhaKm9HuN1wQJSGlFKUaBVNQAFoFkdAk3jDWwu/UXV9lChoBmgJaA9DCF7VWS2wmm9AlIaUUpRoFU02AWgWR0CTeMuy/sVtdX2UKGgGaAloD0MIRtEDH4NBbkCUhpRSlGgVTU8BaBZHQJN47UPQOWl1fZQoaAZoCWgPQwici7/tCW9wQJSGlFKUaBVNQgFoFkdAk3mhQvYe1nV9lChoBmgJaA9DCClAFMzYPXBAlIaUUpRoFU09AWgWR0CTecglF+d9dX2UKGgGaAloD0MIH7qgviWockCUhpRSlGgVTVoBaBZHQJN6VvGZNPB1fZQoaAZoCWgPQwi2vd2SHHpuQJSGlFKUaBVNJQFoFkdAk3qODOC5E3V9lChoBmgJaA9DCGv0aoASB3JAlIaUUpRoFU1PAWgWR0CTe0bX6InCdX2UKGgGaAloD0MITE9Y4kHgckCUhpRSlGgVTT8BaBZHQJN7n6UJOWV1fZQoaAZoCWgPQwijIk4nGURxQJSGlFKUaBVNLgFoFkdAk3zDFdcB2nV9lChoBmgJaA9DCN2YnrDE9XFAlIaUUpRoFU0fAWgWR0CTfNMdcSoPdX2UKGgGaAloD0MImntI+F4BcECUhpRSlGgVTUUBaBZHQJN9PuSfUWl1fZQoaAZoCWgPQwjSUQ5mE7VxQJSGlFKUaBVNVgFoFkdAk32pe/pMYnV9lChoBmgJaA9DCCI2WDgJNXFAlIaUUpRoFU0IAWgWR0CTfe+h4+r3dX2UKGgGaAloD0MIPq946tF7cECUhpRSlGgVTQoBaBZHQJOAdsCT2WZ1fZQoaAZoCWgPQwgIdCZtqmFvQJSGlFKUaBVNfgFoFkdAk4C7EgntwHV9lChoBmgJaA9DCD4+ITtv93BAlIaUUpRoFU00AWgWR0CTggzbN8mbdX2UKGgGaAloD0MIthK6S+IbckCUhpRSlGgVTR4BaBZHQJOCHgAIY3x1fZQoaAZoCWgPQwiZ2HxcmwBwQJSGlFKUaBVNJQFoFkdAk4KI0qH45HV9lChoBmgJaA9DCIwPs5dtl2xAlIaUUpRoFU0TAWgWR0CTgtZlFtsOdX2UKGgGaAloD0MIm3PwTCgNc0CUhpRSlGgVTWEBaBZHQJODcGgSOBF1fZQoaAZoCWgPQwjvAiUFVmZxQJSGlFKUaBVNPwFoFkdAk4P/cJtzjnV9lChoBmgJaA9DCDRnfcoxcHBAlIaUUpRoFU0pAWgWR0CThF4c3l0YdX2UKGgGaAloD0MI41MAjGc2cECUhpRSlGgVTR8BaBZHQJOEdaY/mkp1fZQoaAZoCWgPQwhSmWIOgoFRQJSGlFKUaBVL72gWR0CThLwmVqvedX2UKGgGaAloD0MIIjmZuJV5ckCUhpRSlGgVTSQBaBZHQJOfkDs+mnB1fZQoaAZoCWgPQwiVtrjGZ/hxQJSGlFKUaBVNaQFoFkdAk6GECaJAMXV9lChoBmgJaA9DCP/pBgr8IHFAlIaUUpRoFU1NAWgWR0CToaXenAIqdX2UKGgGaAloD0MIeLRxxFp2cECUhpRSlGgVTV4BaBZHQJOiedFvybx1fZQoaAZoCWgPQwj1K50Pj+RwQJSGlFKUaBVNIQFoFkdAk6NpbY9PlHV9lChoBmgJaA9DCMwKRbqfE25AlIaUUpRoFU0xAWgWR0CTo65NoJzDdX2UKGgGaAloD0MIKh2s/3Mlb0CUhpRSlGgVTQwBaBZHQJOj+wnpjc51fZQoaAZoCWgPQwg+IxEagU5xQJSGlFKUaBVNGwFoFkdAk6TcL4N7SnV9lChoBmgJaA9DCB9N9WQ+GXBAlIaUUpRoFU0yAWgWR0CTpTPpY9xIdX2UKGgGaAloD0MIhnR4COO9cECUhpRSlGgVTRYBaBZHQJOliPIXCTF1fZQoaAZoCWgPQwg1lrA2RoBvQJSGlFKUaBVNCgFoFkdAk6YjcqOLi3V9lChoBmgJaA9DCMgIqHBE3nFAlIaUUpRoFU0PAWgWR0CTpjKCQLeAdX2UKGgGaAloD0MIglX18ruBbUCUhpRSlGgVTRABaBZHQJOmlAGB4D91fZQoaAZoCWgPQwikwthCUIBxQJSGlFKUaBVNMQFoFkdAk6bEgB91EHV9lChoBmgJaA9DCEqzeRwGymJAlIaUUpRoFU3oA2gWR0CTqIjj7yhBdX2UKGgGaAloD0MIYd7jTNNxcUCUhpRSlGgVTUoBaBZHQJOpO6ErXlN1fZQoaAZoCWgPQwjAWUqWk/5wQJSGlFKUaBVNOwFoFkdAk6r0a2nbZnV9lChoBmgJaA9DCCqnPSUnGnFAlIaUUpRoFU0WAWgWR0CTq9NjbzshdX2UKGgGaAloD0MIhjyCGymOb0CUhpRSlGgVS/RoFkdAk6y1Z9uxbHV9lChoBmgJaA9DCIDyd+/oOXBAlIaUUpRoFU1oAWgWR0CTrMDv3JxOdX2UKGgGaAloD0MIpWlQNA8RcECUhpRSlGgVTVoBaBZHQJOtL9YOlO51fZQoaAZoCWgPQwhT6/1GuytuQJSGlFKUaBVNLAFoFkdAk61DMJQcgnV9lChoBmgJaA9DCLMpV3iX4nBAlIaUUpRoFU0TAWgWR0CTrV9OymhudX2UKGgGaAloD0MIqODwgohOcUCUhpRSlGgVTQEBaBZHQJOuH/echDB1fZQoaAZoCWgPQwia0vpbQuNxQJSGlFKUaBVNDwFoFkdAk66U1/DtPnV9lChoBmgJaA9DCKMgeHx7km9AlIaUUpRoFU0cAWgWR0CTr2JE6T4ddX2UKGgGaAloD0MIaf6Y1qaEcUCUhpRSlGgVTRQBaBZHQJOvX8baRIV1fZQoaAZoCWgPQwg1ecpquhBxQJSGlFKUaBVNTQFoFkdAk6+yvovBanV9lChoBmgJaA9DCPncCfZfO3FAlIaUUpRoFU2RAWgWR0CTr+IN3GGVdX2UKGgGaAloD0MIDHOCNvlqc0CUhpRSlGgVS/FoFkdAk7AdOdoWYXV9lChoBmgJaA9DCA8KStHKdHFAlIaUUpRoFU0bAWgWR0CTscDYAbQ1dX2UKGgGaAloD0MI2ZdsPNi9b0CUhpRSlGgVTQ8BaBZHQJOy6cLBsRB1fZQoaAZoCWgPQwjwvioXKp5xQJSGlFKUaBVNCAFoFkdAk7NyhakhzXV9lChoBmgJaA9DCIOJP4p6tHJAlIaUUpRoFU0FAWgWR0CTtB433pOfdX2UKGgGaAloD0MIqoJRSZ24NUCUhpRSlGgVS65oFkdAk7QoqTbFj3V9lChoBmgJaA9DCOPjE7JzZ3FAlIaUUpRoFU0UAWgWR0CTtT5yEL6UdX2UKGgGaAloD0MI/BnerME7b0CUhpRSlGgVTUYBaBZHQJO2STMaCMB1fZQoaAZoCWgPQwi1p+ScWJBuQJSGlFKUaBVNOgFoFkdAk7Zk61b7j3V9lChoBmgJaA9DCI81I4PcgVFAlIaUUpRoFUvyaBZHQJO3Ty7PIGR1fZQoaAZoCWgPQwhD5V/LqyRyQJSGlFKUaBVNSwFoFkdAk7fWN3np0XV9lChoBmgJaA9DCFOT4A1pdGxAlIaUUpRoFU1tAWgWR0CTt+21UlzEdX2UKGgGaAloD0MIiPaxgp+VckCUhpRSlGgVTUIBaBZHQJO4C+GoJiR1fZQoaAZoCWgPQwhckZigRsNwQJSGlFKUaBVNUQFoFkdAk7nZi3G4qnV9lChoBmgJaA9DCKpDboYb9nFAlIaUUpRoFU1mAWgWR0CTueshxHXmdX2UKGgGaAloD0MIbAVNS+xOcECUhpRSlGgVTRQBaBZHQJO6T09QoCx1fZQoaAZoCWgPQwi044bfTdJeQJSGlFKUaBVN6ANoFkdAk7qoSteUp3V9lChoBmgJaA9DCD6XqUlwqHBAlIaUUpRoFU0RAWgWR0CTu9pEhJRPdX2UKGgGaAloD0MIpUqUvSU+cUCUhpRSlGgVTScBaBZHQJO8AJTl1bJ1fZQoaAZoCWgPQwjjVdY2hWtyQJSGlFKUaBVNGAFoFkdAk7ylt0mtyXV9lChoBmgJaA9DCCMw1jewInJAlIaUUpRoFU3EAWgWR0CTvOvcJtzkdX2UKGgGaAloD0MIN45Yi4/AcUCUhpRSlGgVS+hoFkdAk70udTYNAnV9lChoBmgJaA9DCMk7hzLUUW1AlIaUUpRoFU0TAWgWR0CTvWTLns9kdX2UKGgGaAloD0MIp+Zyg+FPckCUhpRSlGgVTTgBaBZHQJO9cmCyyD91ZS4="}, "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": 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-5.17.5-arch1-1-x86_64-with-glibc2.17 #1 SMP PREEMPT Wed, 27 Apr 2022 20:56:11 +0000", "Python": "3.8.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.21.5", "Gym": "0.21.0"}}
ppo-LunarLander-v2-1mln.zip CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:fb5c5296b3f0c67c31f81a5b021ed306d82f61fbd01b2a5ac4c655687c25d3ad
3
- size 143820
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:aca1ba36f3402b3108fde4fdc4999b6141ca3c50535b82d7ed2f8e7e602e203f
3
+ size 144191
ppo-LunarLander-v2-1mln/data CHANGED
@@ -4,19 +4,19 @@
4
  ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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 0x7f0502e5a310>",
8
- "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0502e5a3a0>",
9
- "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0502e5a430>",
10
- "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0502e5a4c0>",
11
- "_build": "<function ActorCriticPolicy._build at 0x7f0502e5a550>",
12
- "forward": "<function ActorCriticPolicy.forward at 0x7f0502e5a5e0>",
13
- "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0502e5a670>",
14
- "_predict": "<function ActorCriticPolicy._predict at 0x7f0502e5a700>",
15
- "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0502e5a790>",
16
- "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0502e5a820>",
17
- "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0502e5a8b0>",
18
  "__abstractmethods__": "frozenset()",
19
- "_abc_impl": "<_abc_data object at 0x7f0502e536c0>"
20
  },
21
  "verbose": 1,
22
  "policy_kwargs": {},
@@ -47,7 +47,7 @@
47
  "_num_timesteps_at_start": 0,
48
  "seed": null,
49
  "action_noise": null,
50
- "start_time": 1652000220.1752121,
51
  "learning_rate": 0.0003,
52
  "tensorboard_log": null,
53
  "lr_schedule": {
@@ -56,7 +56,7 @@
56
  },
57
  "_last_obs": {
58
  ":type:": "<class 'numpy.ndarray'>",
59
- ":serialized:": "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"
60
  },
61
  "_last_episode_starts": {
62
  ":type:": "<class 'numpy.ndarray'>",
@@ -69,7 +69,7 @@
69
  "_current_progress_remaining": -0.015808000000000044,
70
  "ep_info_buffer": {
71
  ":type:": "<class 'collections.deque'>",
72
- ":serialized:": "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"
73
  },
74
  "ep_success_buffer": {
75
  ":type:": "<class 'collections.deque'>",
 
4
  ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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 0x7f0aa0cf0d30>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0aa0cf0dc0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0aa0cf0e50>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0aa0cf0ee0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f0aa0cf0f70>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f0aa0cf3040>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0aa0cf30d0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f0aa0cf3160>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0aa0cf31f0>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0aa0cf3280>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0aa0cf3310>",
18
  "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f0aa0ce5ba0>"
20
  },
21
  "verbose": 1,
22
  "policy_kwargs": {},
 
47
  "_num_timesteps_at_start": 0,
48
  "seed": null,
49
  "action_noise": null,
50
+ "start_time": 1652041112.9235907,
51
  "learning_rate": 0.0003,
52
  "tensorboard_log": null,
53
  "lr_schedule": {
 
56
  },
57
  "_last_obs": {
58
  ":type:": "<class 'numpy.ndarray'>",
59
+ ":serialized:": "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"
60
  },
61
  "_last_episode_starts": {
62
  ":type:": "<class 'numpy.ndarray'>",
 
69
  "_current_progress_remaining": -0.015808000000000044,
70
  "ep_info_buffer": {
71
  ":type:": "<class 'collections.deque'>",
72
+ ":serialized:": "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"
73
  },
74
  "ep_success_buffer": {
75
  ":type:": "<class 'collections.deque'>",
ppo-LunarLander-v2-1mln/policy.optimizer.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:126d0ffdf9cd1639e63f5937848c4ddd348b1fdd45aeb84cda304b82ff919c01
3
- size 84573
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c24049f6dd8e99d4399b00eacb741e042af94fe2cbc27643874e932a34dada94
3
+ size 84829
ppo-LunarLander-v2-1mln/policy.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:c26ec9ee4f77d95f2b78ff08099ec4ff820c50cdb0f160dab00f40f60cf1709d
3
- size 43073
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:01f4688f22bdd9b595f90e24f7fcb3b42a937682c5a74e4c19cb5c72cc8f76bf
3
+ size 43201
ppo-LunarLander-v2-1mln/system_info.txt CHANGED
@@ -2,6 +2,6 @@ OS: Linux-5.17.5-arch1-1-x86_64-with-glibc2.17 #1 SMP PREEMPT Wed, 27 Apr 2022 2
2
  Python: 3.8.13
3
  Stable-Baselines3: 1.5.0
4
  PyTorch: 1.11.0
5
- GPU Enabled: False
6
  Numpy: 1.21.5
7
  Gym: 0.21.0
 
2
  Python: 3.8.13
3
  Stable-Baselines3: 1.5.0
4
  PyTorch: 1.11.0
5
+ GPU Enabled: True
6
  Numpy: 1.21.5
7
  Gym: 0.21.0
replay.mp4 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c29b775877c9a227f8af3e2d85359621ecefdff2435adac5d8a8d5a729ad2d33
3
+ size 214382
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": -80.59060994540341, "std_reward": 42.59550653898667, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-08T22:17:12.590261"}
 
1
+ {"mean_reward": 252.42419757747498, "std_reward": 24.341753238907636, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-08T22:35:48.336690"}