bvk1ng commited on
Commit
f00c817
1 Parent(s): e6c35d9

Adding::Higher timesteps trained PPO based agent

Browse files
README.md CHANGED
@@ -6,7 +6,7 @@ tags:
6
  - reinforcement-learning
7
  - stable-baselines3
8
  model-index:
9
- - name: PPO
10
  results:
11
  - task:
12
  type: reinforcement-learning
@@ -16,13 +16,13 @@ model-index:
16
  type: LunarLander-v2
17
  metrics:
18
  - type: mean_reward
19
- value: 275.09 +/- 22.90
20
  name: mean_reward
21
  verified: false
22
  ---
23
 
24
- # **PPO** Agent playing **LunarLander-v2**
25
- This is a trained model of a **PPO** agent playing **LunarLander-v2**
26
  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
 
28
  ## Usage (with Stable-baselines3)
 
6
  - reinforcement-learning
7
  - stable-baselines3
8
  model-index:
9
+ - name: Proximal Policy Optimisation (PPO)
10
  results:
11
  - task:
12
  type: reinforcement-learning
 
16
  type: LunarLander-v2
17
  metrics:
18
  - type: mean_reward
19
+ value: 290.88 +/- 17.28
20
  name: mean_reward
21
  verified: false
22
  ---
23
 
24
+ # **Proximal Policy Optimisation (PPO)** Agent playing **LunarLander-v2**
25
+ This is a trained model of a **Proximal Policy Optimisation (PPO)** agent playing **LunarLander-v2**
26
  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
 
28
  ## Usage (with Stable-baselines3)
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 0x7f6a62984160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6a629841f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6a62984280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6a62984310>", "_build": "<function ActorCriticPolicy._build at 0x7f6a629843a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f6a62984430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6a629844c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6a62984550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6a629845e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6a62984670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6a62984700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6a6297f600>"}, "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": 1670331226301175288, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_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:": "gAWVNRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIWMfxQyVwcUCUhpRSlIwBbJRL6YwBdJRHQKAxzPGACnx1fZQoaAZoCWgPQwhkHvmDwcRxQJSGlFKUaBVL6WgWR0CgMfnPeHi4dX2UKGgGaAloD0MIPPVIg5v4cECUhpRSlGgVS91oFkdAoDI0yrPt2XV9lChoBmgJaA9DCJkrg2rDw3JAlIaUUpRoFU0PAWgWR0CgMnvsZ5zHdX2UKGgGaAloD0MIYajDCreQbUCUhpRSlGgVS+1oFkdAoDKAE8q4IHV9lChoBmgJaA9DCNTS3AphQHJAlIaUUpRoFUvPaBZHQKAyk43m3fB1fZQoaAZoCWgPQwjl0CLb+SRxQJSGlFKUaBVNCgFoFkdAoDKY8Md92HV9lChoBmgJaA9DCO7rwDmj0nJAlIaUUpRoFUvraBZHQKAynjdYW+J1fZQoaAZoCWgPQwjAety3mvxyQJSGlFKUaBVNGgFoFkdAoDLX5FgDzXV9lChoBmgJaA9DCLR1cLB3FnNAlIaUUpRoFUvzaBZHQKAy3wS8J2N1fZQoaAZoCWgPQwgC85Apn3BxQJSGlFKUaBVNGAFoFkdAoDQq/IsAenV9lChoBmgJaA9DCDUMHxHTzXFAlIaUUpRoFU0BAWgWR0CgNFCT2WY4dX2UKGgGaAloD0MIZED2encWcUCUhpRSlGgVS+xoFkdAoDTA79ycTnV9lChoBmgJaA9DCFfsL7snwG5AlIaUUpRoFUvnaBZHQKA08fZElVt1fZQoaAZoCWgPQwg66BIO/atxQJSGlFKUaBVL4mgWR0CgNWYNZvDQdX2UKGgGaAloD0MIylTBqKQTcECUhpRSlGgVTTgBaBZHQKA1pT2FnI11fZQoaAZoCWgPQwjL2qZ43EtwQJSGlFKUaBVL+mgWR0CgNgx/d69kdX2UKGgGaAloD0MIs5YC0n61ckCUhpRSlGgVS9toFkdAoDZS5I6KcnV9lChoBmgJaA9DCBjONczQFXFAlIaUUpRoFU0nAWgWR0CgNmAZKnNxdX2UKGgGaAloD0MIpHA9CtcncUCUhpRSlGgVS/VoFkdAoDZfLRrrPnV9lChoBmgJaA9DCGgFhqzu33FAlIaUUpRoFU0IAWgWR0CgNpEeQuEmdX2UKGgGaAloD0MIIH7+ezBpcUCUhpRSlGgVTQgBaBZHQKA2rQm/nGN1fZQoaAZoCWgPQwi8QbRWNBxvQJSGlFKUaBVL9mgWR0CgNrHVXmvGdX2UKGgGaAloD0MIrkoi+yCHckCUhpRSlGgVTRUBaBZHQKA20SOinHh1fZQoaAZoCWgPQwhfQ3BcxmBzQJSGlFKUaBVNJAFoFkdAoDb92V3Ux3V9lChoBmgJaA9DCBIvT+fKwHFAlIaUUpRoFU0BAWgWR0CgOEoDoyKvdX2UKGgGaAloD0MIE0NyMjF/c0CUhpRSlGgVS/5oFkdAoDhcx46fa3V9lChoBmgJaA9DCIOI1LSLrG9AlIaUUpRoFUvzaBZHQKA4xIUahpR1fZQoaAZoCWgPQwj68CxBBt1wQJSGlFKUaBVL5WgWR0CgOT6SLZSOdX2UKGgGaAloD0MIYobGE4GscUCUhpRSlGgVTQ4BaBZHQKA50tjCpFV1fZQoaAZoCWgPQwjpZKn1fp5vQJSGlFKUaBVL8WgWR0CgOf4150KadX2UKGgGaAloD0MI4QhSKTYyc0CUhpRSlGgVS+RoFkdAoDoQg5imVXV9lChoBmgJaA9DCNHoDmJnonBAlIaUUpRoFUvnaBZHQKA6Ko+fRNR1fZQoaAZoCWgPQwh9eJYgo2JwQJSGlFKUaBVL92gWR0CgOnW0Z3s5dX2UKGgGaAloD0MI3q0s0RnocUCUhpRSlGgVS+RoFkdAoDp7EzfrKXV9lChoBmgJaA9DCNnMIamFnm9AlIaUUpRoFUvsaBZHQKA6eup0fYB1fZQoaAZoCWgPQwgpr5XQXV5JQJSGlFKUaBVN6ANoFkdAoDqTW3BpH3V9lChoBmgJaA9DCI+pu7JL8HFAlIaUUpRoFUvsaBZHQKA6mbADaGp1fZQoaAZoCWgPQwi0xwvpsDNwQJSGlFKUaBVL5mgWR0CgOqclPacqdX2UKGgGaAloD0MItU5cjldBc0CUhpRSlGgVS/BoFkdAoDrnEbYK6XV9lChoBmgJaA9DCFpJK74h1G5AlIaUUpRoFUvjaBZHQKBWlcX3xnZ1fZQoaAZoCWgPQwiLNzKPvLhxQJSGlFKUaBVL5mgWR0CgVrxOclPadX2UKGgGaAloD0MIXRq/8EqmbUCUhpRSlGgVS+ZoFkdAoFeUQVbiZXV9lChoBmgJaA9DCPK1Z5YEZXNAlIaUUpRoFU0LAWgWR0CgV7xbr1M/dX2UKGgGaAloD0MILliqCzjRcECUhpRSlGgVS+ZoFkdAoFhfxri2lXV9lChoBmgJaA9DCHCZ02VxrHFAlIaUUpRoFUv7aBZHQKBYanbZezF1fZQoaAZoCWgPQwiFfTuJCJpyQJSGlFKUaBVL82gWR0CgWG6AOJ+EdX2UKGgGaAloD0MIxD9s6ZEzckCUhpRSlGgVS/JoFkdAoFh5f0Eov3V9lChoBmgJaA9DCNobfGEyvG9AlIaUUpRoFUvoaBZHQKBYrS/j81p1fZQoaAZoCWgPQwjXv+szZ5BuQJSGlFKUaBVL82gWR0CgWNYBmwqzdX2UKGgGaAloD0MII57sZgY1c0CUhpRSlGgVS+9oFkdAoFjlCmdiD3V9lChoBmgJaA9DCBpNLsZAX3NAlIaUUpRoFUvxaBZHQKBY+wXZXdV1fZQoaAZoCWgPQwjJPPIHw+JyQJSGlFKUaBVL/mgWR0CgWP8+aBqcdX2UKGgGaAloD0MI8+fbgqXObUCUhpRSlGgVS+doFkdAoFkjaPCEYnV9lChoBmgJaA9DCLGk3H3OpXFAlIaUUpRoFU0AAWgWR0CgWSbdi2DydX2UKGgGaAloD0MIey3ovbGAb0CUhpRSlGgVS9xoFkdAoFoJ59mYjXV9lChoBmgJaA9DCKSLTSuFuG9AlIaUUpRoFUvraBZHQKBaKEvkBCF1fZQoaAZoCWgPQwhOmgZFM2xwQJSGlFKUaBVL42gWR0CgWupVsDW9dX2UKGgGaAloD0MIPKOtSiLXcUCUhpRSlGgVS95oFkdAoFurAaef7XV9lChoBmgJaA9DCKBU+3S8R3FAlIaUUpRoFUvqaBZHQKBb2J0GNaR1fZQoaAZoCWgPQwgMPWL03LlvQJSGlFKUaBVL+WgWR0CgXCXbM5fddX2UKGgGaAloD0MIaVIKuj0RbkCUhpRSlGgVS+toFkdAoFwuFJxvN3V9lChoBmgJaA9DCCzy64eYj3JAlIaUUpRoFUv9aBZHQKBcSJdjXnR1fZQoaAZoCWgPQwidn+I4cNVyQJSGlFKUaBVL+mgWR0CgXJznRsuWdX2UKGgGaAloD0MIfewuUBJGckCUhpRSlGgVS/hoFkdAoFzGnAIppnV9lChoBmgJaA9DCIZyol2FVnFAlIaUUpRoFUv6aBZHQKBcyfI0ZWJ1fZQoaAZoCWgPQwj/dW7azCNyQJSGlFKUaBVNDAFoFkdAoFz1SqEOAnV9lChoBmgJaA9DCKw5QDBHO3JAlIaUUpRoFU1cAWgWR0CgXRfCyhSMdX2UKGgGaAloD0MIZCR7hNqJcECUhpRSlGgVTQYBaBZHQKBdIvPC2tx1fZQoaAZoCWgPQwh+VpkpLb9vQJSGlFKUaBVL3GgWR0CgXZ9xIatLdX2UKGgGaAloD0MI8+ZwrTbccUCUhpRSlGgVS+RoFkdAoF2igVXV9XV9lChoBmgJaA9DCJcA/FOqnXFAlIaUUpRoFUvgaBZHQKBebpV0cOt1fZQoaAZoCWgPQwitwfuqnK1xQJSGlFKUaBVL2mgWR0CgXxGIbfgrdX2UKGgGaAloD0MIBoGVQ4sAZECUhpRSlGgVTegDaBZHQKBfFjc2zfJ1fZQoaAZoCWgPQwgKZ7eWiR5wQJSGlFKUaBVL3WgWR0CgX47a7EpBdX2UKGgGaAloD0MI7e9sj94bcUCUhpRSlGgVS/xoFkdAoF+2p84Pw3V9lChoBmgJaA9DCDiCVIrdNnNAlIaUUpRoFUvZaBZHQKBf3SWJJoV1fZQoaAZoCWgPQwjeOCnM+wpxQJSGlFKUaBVL0mgWR0CgX+jaGpMpdX2UKGgGaAloD0MIbMuAsxRlckCUhpRSlGgVS/poFkdAoF/w4ZMtb3V9lChoBmgJaA9DCP+R6dApNnFAlIaUUpRoFU0FAWgWR0CgYDHE/B3zdX2UKGgGaAloD0MID9Qpj+6FcECUhpRSlGgVS+hoFkdAoGBitxMnJHV9lChoBmgJaA9DCNjTDn8Ng3FAlIaUUpRoFUveaBZHQKBgcR02cax1fZQoaAZoCWgPQwh5sTBEzk9xQJSGlFKUaBVL72gWR0CgYKYp+c6OdX2UKGgGaAloD0MIuamB5rOzcUCUhpRSlGgVTR0BaBZHQKBg9Wf9P1t1fZQoaAZoCWgPQwjsoX2sYNttQJSGlFKUaBVL42gWR0CgYP2ovSMMdX2UKGgGaAloD0MI+FPjpVvMckCUhpRSlGgVTQ0BaBZHQKBhgRJVbRp1fZQoaAZoCWgPQwjcZFQZhlRwQJSGlFKUaBVL/2gWR0CgYg9ovi97dX2UKGgGaAloD0MILj2a6gl7cUCUhpRSlGgVS+5oFkdAoGJdJ6IFeXV9lChoBmgJaA9DCG4VxEDXuHBAlIaUUpRoFUvwaBZHQKBiandfsu51fZQoaAZoCWgPQwhBDHTti9twQJSGlFKUaBVL6WgWR0CgYtAymALBdX2UKGgGaAloD0MIayxhbQxkcUCUhpRSlGgVS9toFkdAoGL1XRw6yXV9lChoBmgJaA9DCAfTMHxEwG9AlIaUUpRoFUv+aBZHQKBjWFKTSst1fZQoaAZoCWgPQwgOorWiDUtxQJSGlFKUaBVL82gWR0CgY2li8WbgdX2UKGgGaAloD0MIAyMva2Jrc0CUhpRSlGgVTQIBaBZHQKBjjGIbfgt1fZQoaAZoCWgPQwih2uBE9BhzQJSGlFKUaBVL7mgWR0CgY5mMfigkdX2UKGgGaAloD0MITYV4JN5yb0CUhpRSlGgVS/ZoFkdAoGP6bx3FDXV9lChoBmgJaA9DCGaFIt0PR3BAlIaUUpRoFUvraBZHQKBkC2w3YL91fZQoaAZoCWgPQwgDXmbYqFFyQJSGlFKUaBVL+WgWR0CgZJqJMxoJdX2UKGgGaAloD0MIXg8mxccOcUCUhpRSlGgVS/1oFkdAoGS4vBacJHV9lChoBmgJaA9DCF3Cobe4WXJAlIaUUpRoFUvcaBZHQKBk0ir1dxB1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 620, "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": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "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 0x7fcd7990fb50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcd7990fbe0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcd7990fc70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcd7990fd00>", "_build": "<function ActorCriticPolicy._build at 0x7fcd7990fd90>", "forward": "<function ActorCriticPolicy.forward at 0x7fcd7990fe20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcd7990feb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcd7990ff40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcd79910040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcd799100d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcd79910160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fcd7a088480>"}, "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1670546542282878394, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV8wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMYC9ob21lL2RhcnRoL21pbmljb25kYTMvZW52cy9tbGVudi9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgQBlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMYC9ob21lL2RhcnRoL21pbmljb25kYTMvZW52cy9tbGVudi9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": null, "_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": 1230, "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": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWV8wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMYC9ob21lL2RhcnRoL21pbmljb25kYTMvZW52cy9tbGVudi9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgQBlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMYC9ob21lL2RhcnRoL21pbmljb25kYTMvZW52cy9tbGVudi9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.15.0-56-generic-x86_64-with-glibc2.35 #62-Ubuntu SMP Tue Nov 22 19:54:14 UTC 2022", "Python": "3.10.8", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0", "GPU Enabled": "True", "Numpy": "1.23.4", "Gym": "0.21.0"}}
lunarlander_v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4907cec3764430f88df6b6295dc27a510578eedbc5f14e17baf120475bb1715e
3
+ size 146444
lunarlander_v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
lunarlander_v2/data ADDED
@@ -0,0 +1,91 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
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 0x7fcd7990fb50>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcd7990fbe0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcd7990fc70>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcd7990fd00>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fcd7990fd90>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fcd7990fe20>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcd7990feb0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fcd7990ff40>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcd79910040>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcd799100d0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcd79910160>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc._abc_data object at 0x7fcd7a088480>"
20
+ },
21
+ "verbose": 1,
22
+ "policy_kwargs": {},
23
+ "observation_space": {
24
+ ":type:": "<class 'gym.spaces.box.Box'>",
25
+ ":serialized:": "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",
26
+ "dtype": "float32",
27
+ "_shape": [
28
+ 8
29
+ ],
30
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
31
+ "high": "[inf inf inf inf inf inf inf inf]",
32
+ "bounded_below": "[False False False False False False False False]",
33
+ "bounded_above": "[False False False False False False False False]",
34
+ "_np_random": null
35
+ },
36
+ "action_space": {
37
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
38
+ ":serialized:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
39
+ "n": 4,
40
+ "_shape": [],
41
+ "dtype": "int64",
42
+ "_np_random": null
43
+ },
44
+ "n_envs": 16,
45
+ "num_timesteps": 2015232,
46
+ "_total_timesteps": 2000000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1670546542282878394,
51
+ "learning_rate": 0.0003,
52
+ "tensorboard_log": null,
53
+ "lr_schedule": {
54
+ ":type:": "<class 'function'>",
55
+ ":serialized:": "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"
56
+ },
57
+ "_last_obs": null,
58
+ "_last_episode_starts": {
59
+ ":type:": "<class 'numpy.ndarray'>",
60
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
61
+ },
62
+ "_last_original_obs": null,
63
+ "_episode_num": 0,
64
+ "use_sde": false,
65
+ "sde_sample_freq": -1,
66
+ "_current_progress_remaining": -0.007616000000000067,
67
+ "ep_info_buffer": {
68
+ ":type:": "<class 'collections.deque'>",
69
+ ":serialized:": "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"
70
+ },
71
+ "ep_success_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
74
+ },
75
+ "_n_updates": 1230,
76
+ "n_steps": 1024,
77
+ "gamma": 0.999,
78
+ "gae_lambda": 0.98,
79
+ "ent_coef": 0.01,
80
+ "vf_coef": 0.5,
81
+ "max_grad_norm": 0.5,
82
+ "batch_size": 64,
83
+ "n_epochs": 10,
84
+ "clip_range": {
85
+ ":type:": "<class 'function'>",
86
+ ":serialized:": "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"
87
+ },
88
+ "clip_range_vf": null,
89
+ "normalize_advantage": true,
90
+ "target_kl": null
91
+ }
lunarlander_v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d56bdd4056d4171973e77e29964107578b1fa2a639e2cfb87aef7dc3bb03ea81
3
+ size 88057
lunarlander_v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f0c771b7d8986de8a718512c2fcc8bc9756a8a456450e41a4d32f8df4e15ba81
3
+ size 43201
lunarlander_v2/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
lunarlander_v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ OS: Linux-5.15.0-56-generic-x86_64-with-glibc2.35 #62-Ubuntu SMP Tue Nov 22 19:54:14 UTC 2022
2
+ Python: 3.10.8
3
+ Stable-Baselines3: 1.6.2
4
+ PyTorch: 1.13.0
5
+ GPU Enabled: True
6
+ Numpy: 1.23.4
7
+ Gym: 0.21.0
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": 275.08591998409804, "std_reward": 22.901268863836453, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-06T13:28:46.186107"}
 
1
+ {"mean_reward": 290.8752914142443, "std_reward": 17.27576260261546, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-09T17:03:19.731271"}