Notebooks
H
Hugging Face
Reinforcement Learning With Diffusers

Reinforcement Learning With Diffusers

hf-notebooksdiffusers

Introduction

This notebook is designed to run inference on the Diffuser planning model for model-based RL. The notebook is modified from the authors' original. For those new to reinforcement learning, consider checking out the HuggingFace Reinforcement Learning Course for a primer.

Colab made by Nathan Lambert and Ben Glickenhaus.

diffusers_library

Installing Packages

apt-get install requirements

These requirements primarily pertain to install mujoco and run it in the colab. Source was inspired by this (fairly recent) demo.

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Reading package lists... Done
Building dependency tree       
Reading state information... Done
libglew-dev is already the newest version (2.0.0-5).
libgl1-mesa-dev is already the newest version (20.0.8-0ubuntu1~18.04.1).
libgl1-mesa-glx is already the newest version (20.0.8-0ubuntu1~18.04.1).
libosmesa6-dev is already the newest version (20.0.8-0ubuntu1~18.04.1).
software-properties-common is already the newest version (0.96.24.32.18).
The following package was automatically installed and is no longer required:
  libnvidia-common-460
Use 'apt autoremove' to remove it.
0 upgraded, 0 newly installed, 0 to remove and 27 not upgraded.
Reading package lists... Done
Building dependency tree       
Reading state information... Done
patchelf is already the newest version (0.9-1).
The following package was automatically installed and is no longer required:
  libnvidia-common-460
Use 'apt autoremove' to remove it.
0 upgraded, 0 newly installed, 0 to remove and 27 not upgraded.

Install Diffusers

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/content
Found existing installation: diffusers 0.5.0.dev0
Uninstalling diffusers-0.5.0.dev0:
  Successfully uninstalled diffusers-0.5.0.dev0
Cloning into 'diffusers'...
remote: Enumerating objects: 10356, done.
remote: Counting objects: 100% (502/502), done.
remote: Compressing objects: 100% (251/251), done.
remote: Total 10356 (delta 277), reused 384 (delta 201), pack-reused 9854
Receiving objects: 100% (10356/10356), 7.81 MiB | 17.77 MiB/s, done.
Resolving deltas: 100% (6885/6885), done.
  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
    Preparing wheel metadata ... done
  Building wheel for diffusers (PEP 517) ... done
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pip install requirements

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Import D4RL to initialize Mujoco

Mujoco is a physics simulator used extensively in reinforcement learning research. Here, we import D4RL (a library of datasets and environments for Offline RL), which results in the building of Mujoco.

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Warning: Gym version v0.24.1 has a number of critical issues with `gym.make` such that environment observation and action spaces are incorrectly evaluated, raising incorrect errors and warning . It is recommend to downgrading to v0.23.1 or upgrading to v0.25.1
Warning: Flow failed to import. Set the environment variable D4RL_SUPPRESS_IMPORT_ERROR=1 to suppress this message.
No module named 'flow'
Warning: CARLA failed to import. Set the environment variable D4RL_SUPPRESS_IMPORT_ERROR=1 to suppress this message.
No module named 'carla'
/usr/local/lib/python3.7/dist-packages/gym/envs/registration.py:416: UserWarning: WARN: The `registry.env_specs` property along with `EnvSpecTree` is deprecated. Please use `registry` directly as a dictionary instead.
  "The `registry.env_specs` property along with `EnvSpecTree` is deprecated. Please use `registry` directly as a dictionary instead."

Environment & Model Setup

In this section, we will create the environment, handle the data, and run the diffusion model.

Imports

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Create environment

This colab is designed to run with pretrained models from the hopper environment. As more models are trained, this can be extended.

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/usr/local/lib/python3.7/dist-packages/gym/envs/mujoco/mujoco_env.py:47: UserWarning: WARN: This version of the mujoco environments depends on the mujoco-py bindings, which are no longer maintained and may stop working. Please upgrade to the v4 versions of the environments (which depend on the mujoco python bindings instead), unless you are trying to precisely replicate previous works).
  "This version of the mujoco environments depends "
/usr/local/lib/python3.7/dist-packages/gym/spaces/box.py:112: UserWarning: WARN: Box bound precision lowered by casting to float32
  logger.warn(f"Box bound precision lowered by casting to {self.dtype}")
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:70: UserWarning: WARN: Agent's minimum action space value is -infinity. This is probably too low.
  "Agent's minimum action space value is -infinity. This is probably too low."
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:74: UserWarning: WARN: Agent's maximum action space value is infinity. This is probably too high
  "Agent's maximum action space value is infinity. This is probably too high"
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:98: UserWarning: WARN: We recommend you to use a symmetric and normalized Box action space (range=[-1, 1]) https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html
  "We recommend you to use a symmetric and normalized Box action space (range=[-1, 1]) "
load datafile:  19%|█▉        | 4/21 [00:00<00:03,  5.38it/s]/usr/local/lib/python3.7/dist-packages/h5py/_hl/dataset.py:767: DeprecationWarning: Passing None into shape arguments as an alias for () is deprecated.
  arr = numpy.ndarray(selection.mshape, dtype=new_dtype)
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Define constants

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Helper functions

  • normalize scales the state values corresponding to the training data-set in D4RL,
  • de_normalize unscales the data for correct rendering,
  • to_torch handles casting to torch for both numpy arrays and dicts (used for conditionning the model, see reset_x0).
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Sample env. initial state

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Run the Diffusion Process -- from Scratch

Initialize model

In this section, we create a scheduler and load a pretrained model from the Hub. An important detail in the RL application space is to save conditions which will allow the model to optimize trajectories only from the current state (which is cruical to making decisions!).

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Planning helper function

reset_x0 is used to constrain the diffusion process to trajectories starting at the current state of the agent. Without this, the diffusion process would generate arbitrary high-reward trajectories, rather than trajectories beginning at the current state.

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Setup for denoising

conditions is the variable used to hold the first state of the planned trajectories to the current state (it is passed into reset_x0).

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Sample initial noise

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Generate trajectories

The diffusion process for trajectories has 4 central components:

  1. sampling an predicted original sample from the model (note that this model directly predicts the sample, rather than the error term epsilon used in many diffusion models),
  2. use the scheduler to predict the sample at the previous timestep,
  3. [optional] add posterior noise to the sample,
  4. condition the trajectory to constrain the initial state.
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torch.Size([4, 128, 14])

Render the samples

Renderering Tools

Rendering from Mujoco is historically not easy. Here is a modified version from the original paper. Additionally, a TODO is to investigate this web-based viewer.

Video helpers
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Renderer helpers

These functions involve setting the state of the environment and reading it out in a pixel form.

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Rendering class

Use the previously defined helpers to programatically render pixel sequences from a trajectory of states. This class takes the re-scaled outputs of the diffusion process and visualizes them.

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Show Plans

This section renders 4 trajectories chosen from the same initial state in the environment.

Initialize renderer class for the environment
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Show the video

Show the states generated by the diffusion model in the real environment. Not that the actions are dropped from the data.

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Run Value Guided Diffusion -- with Pipeline

In this section, we repeat the above code, but we use a pre-trained pipeline in Diffusers!

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/usr/local/lib/python3.7/dist-packages/gym/envs/mujoco/mujoco_env.py:47: UserWarning: WARN: This version of the mujoco environments depends on the mujoco-py bindings, which are no longer maintained and may stop working. Please upgrade to the v4 versions of the environments (which depend on the mujoco python bindings instead), unless you are trying to precisely replicate previous works).
  "This version of the mujoco environments depends "
/usr/local/lib/python3.7/dist-packages/gym/spaces/box.py:112: UserWarning: WARN: Box bound precision lowered by casting to float32
  logger.warn(f"Box bound precision lowered by casting to {self.dtype}")
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:70: UserWarning: WARN: Agent's minimum action space value is -infinity. This is probably too low.
  "Agent's minimum action space value is -infinity. This is probably too low."
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:74: UserWarning: WARN: Agent's maximum action space value is infinity. This is probably too high
  "Agent's maximum action space value is infinity. This is probably too high"
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:98: UserWarning: WARN: We recommend you to use a symmetric and normalized Box action space (range=[-1, 1]) https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html
  "We recommend you to use a symmetric and normalized Box action space (range=[-1, 1]) "
load datafile:  19%|█▉        | 4/21 [00:00<00:03,  5.16it/s]/usr/local/lib/python3.7/dist-packages/h5py/_hl/dataset.py:767: DeprecationWarning: Passing None into shape arguments as an alias for () is deprecated.
  arr = numpy.ndarray(selection.mshape, dtype=new_dtype)
load datafile: 100%|██████████| 21/21 [00:01<00:00, 15.05it/s]
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Load the pipeline!

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The config attributes {'args': ['diffusers', 'DDPMScheduler'], 'kwargs': ['diffusers', 'PNDMScheduler']} were passed to ValueGuidedDiffuserPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
load datafile: 100%|██████████| 21/21 [00:01<00:00, 13.65it/s]
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/usr/local/lib/python3.7/dist-packages/gym/core.py:201: DeprecationWarning: WARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed)` instead.
  "Function `env.seed(seed)` is marked as deprecated and will be removed in the future. "
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:217: UserWarning: WARN: Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator. 
  "Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator. "
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:229: UserWarning: WARN: Future gym versions will require that `Env.reset` can be passed `return_info` to return information from the environment resetting.
  "Future gym versions will require that `Env.reset` can be passed `return_info` to return information from the environment resetting."
/usr/local/lib/python3.7/dist-packages/gym/utils/passive_env_checker.py:234: UserWarning: WARN: Future gym versions will require that `Env.reset` can be passed `options` to allow the environment initialisation to be passed additional information.
  "Future gym versions will require that `Env.reset` can be passed `options` to allow the environment initialisation to be passed additional information."
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Step: 0, Reward: 0.9613453417633487, Total Reward: 0.9613453417633487, Score: 0.006228869141685884, Total Score: 0.006228869141685884

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Step: 1, Reward: 0.9905865761281842, Total Reward: 1.951931917891533, Score: 0.006524252144941468, Total Score: 0.012753121286627353

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Step: 2, Reward: 1.09243849732667, Total Reward: 3.044370415218203, Score: 0.00682861981088834, Total Score: 0.019581741097515693

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Step: 3, Reward: 1.1971688596663932, Total Reward: 4.241539274884596, Score: 0.007164282501696702, Total Score: 0.026746023599212396

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Step: 4, Reward: 1.216392029159964, Total Reward: 5.4579313040445605, Score: 0.007532124647292049, Total Score: 0.03427814824650444

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Step: 5, Reward: 1.1837624373676066, Total Reward: 6.641693741412167, Score: 0.007905873304616767, Total Score: 0.042184021551121206

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Step: 6, Reward: 1.316867289864599, Total Reward: 7.9585610312767665, Score: 0.008269596192428782, Total Score: 0.05045361774354999

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Step: 7, Reward: 1.458532898214762, Total Reward: 9.417093929491529, Score: 0.00867421688186361, Total Score: 0.0591278346254136

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Step: 8, Reward: 1.5655737561538712, Total Reward: 10.9826676856454, Score: 0.009122365751063418, Total Score: 0.06825020037647701

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Step: 9, Reward: 1.6407680269786085, Total Reward: 12.623435712624008, Score: 0.009603403998008702, Total Score: 0.07785360437448571

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Step: 10, Reward: 1.7056611798297208, Total Reward: 14.329096892453729, Score: 0.010107546439231438, Total Score: 0.08796115081371715

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Step: 11, Reward: 1.7747508738212663, Total Reward: 16.103847766274995, Score: 0.010631627952863603, Total Score: 0.09859277876658075

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Step: 12, Reward: 1.818083363081429, Total Reward: 17.921931129356423, Score: 0.01117693796828244, Total Score: 0.10976971673486319

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Step: 13, Reward: 1.816563587926592, Total Reward: 19.738494717283015, Score: 0.011735562325863405, Total Score: 0.1215052790607266

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Step: 14, Reward: 1.8434045339664338, Total Reward: 21.58189925124945, Score: 0.012293719717277263, Total Score: 0.13379899877800386

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Step: 15, Reward: 1.8763610133755022, Total Reward: 23.45826026462495, Score: 0.012860124258707918, Total Score: 0.14665912303671177

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Step: 16, Reward: 1.8580889190962222, Total Reward: 25.316349183721172, Score: 0.013436655009151793, Total Score: 0.16009577804586356

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Step: 17, Reward: 1.833873406539959, Total Reward: 27.15022259026113, Score: 0.014007571475269827, Total Score: 0.17410334952113338

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Step: 18, Reward: 1.7923612227351886, Total Reward: 28.942583812996318, Score: 0.01457104748215484, Total Score: 0.18867439700328822

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Step: 19, Reward: 1.7772864941994806, Total Reward: 30.719870307195798, Score: 0.015121768453995469, Total Score: 0.20379616545728368

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Step: 20, Reward: 1.7596802514374612, Total Reward: 32.47955055863326, Score: 0.01566785756422019, Total Score: 0.21946402302150386

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Step: 21, Reward: 1.7213467511037084, Total Reward: 34.200897309736966, Score: 0.0162085369796795, Total Score: 0.23567256000118336

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Step: 22, Reward: 1.681650865266795, Total Reward: 35.88254817500376, Score: 0.016737438042488645, Total Score: 0.25240999804367203

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Step: 23, Reward: 1.649656828943054, Total Reward: 37.532205003946814, Score: 0.017254142146030386, Total Score: 0.2696641401897024

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Step: 24, Reward: 1.6520971180163586, Total Reward: 39.18430212196317, Score: 0.017761015760854884, Total Score: 0.2874251559505573

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Step: 25, Reward: 1.6532160502693138, Total Reward: 40.837518172232485, Score: 0.0182686391789852, Total Score: 0.3056937951295425

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Step: 26, Reward: 1.6534424303321889, Total Reward: 42.49096060256468, Score: 0.0187766064002786, Total Score: 0.3244704015298211

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Step: 27, Reward: 1.6543163640247096, Total Reward: 44.145276966589385, Score: 0.019284643179118023, Total Score: 0.34375504470893914

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Step: 28, Reward: 1.6274647523947772, Total Reward: 45.772741718984165, Score: 0.019792948482854303, Total Score: 0.36354799319179343

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Step: 29, Reward: 1.6125653440731562, Total Reward: 47.38530706305732, Score: 0.020293003359464205, Total Score: 0.38384099655125764

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Step: 30, Reward: 1.6173703384364473, Total Reward: 49.00267740149376, Score: 0.0207884802433533, Total Score: 0.4046294767946109

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Step: 31, Reward: 1.5497623762580708, Total Reward: 50.552439777751836, Score: 0.02128543350997813, Total Score: 0.42591491030458906

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Step: 32, Reward: 1.490298587276498, Total Reward: 52.042738365028335, Score: 0.0217616135517849, Total Score: 0.44767652385637396

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Step: 33, Reward: 1.4584480178869372, Total Reward: 53.50118638291527, Score: 0.022219522747714257, Total Score: 0.46989604660408824

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Step: 34, Reward: 1.4048398211769302, Total Reward: 54.9060262040922, Score: 0.022667645536581578, Total Score: 0.4925636921406698

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Step: 35, Reward: 1.378891084714629, Total Reward: 56.28491728880683, Score: 0.023099296669057166, Total Score: 0.5156629888097269

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Step: 36, Reward: 1.3393234239492198, Total Reward: 57.62424071275605, Score: 0.023522974791861884, Total Score: 0.5391859636015888

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Step: 37, Reward: 1.2729857307511654, Total Reward: 58.897226443507215, Score: 0.023934495353839142, Total Score: 0.563120458955428

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Step: 38, Reward: 1.2637156051988185, Total Reward: 60.16094204870603, Score: 0.024325632993889564, Total Score: 0.5874460919493175

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Step: 39, Reward: 1.2776175812728203, Total Reward: 61.438559629978855, Score: 0.0247139222948393, Total Score: 0.6121600142441568

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Step: 40, Reward: 1.203964270030611, Total Reward: 62.642523900009465, Score: 0.02510648311744263, Total Score: 0.6372664973615995

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Step: 41, Reward: 1.092016290948236, Total Reward: 63.7345401909577, Score: 0.025476413221063608, Total Score: 0.6627429105826631

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Step: 42, Reward: 1.1296472681807004, Total Reward: 64.8641874591384, Score: 0.0258119461847254, Total Score: 0.6885548567673885

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Step: 43, Reward: 1.1803122177054457, Total Reward: 66.04449967684386, Score: 0.026159041643764744, Total Score: 0.7147138984111532

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Step: 44, Reward: 1.200053185050725, Total Reward: 67.24455286189458, Score: 0.02652170441696297, Total Score: 0.7412356028281162

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Step: 45, Reward: 1.196837866003687, Total Reward: 68.44139072789827, Score: 0.026890432800476552, Total Score: 0.7681260356285927

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Step: 46, Reward: 1.122069119979826, Total Reward: 69.5634598478781, Score: 0.027258173244947545, Total Score: 0.7953842088735402

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Step: 47, Reward: 1.0683895229010438, Total Reward: 70.63184937077914, Score: 0.027602940241906255, Total Score: 0.8229871491154465

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Step: 48, Reward: 1.0614508004688126, Total Reward: 71.69330017124796, Score: 0.027931213643993428, Total Score: 0.8509183627594399

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Step: 49, Reward: 0.9609488901545228, Total Reward: 72.65424906140248, Score: 0.028257355053983954, Total Score: 0.8791757178134239

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Step: 50, Reward: 0.9235699583514453, Total Reward: 73.57781901975393, Score: 0.028552616243504376, Total Score: 0.9077283340569282

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Step: 51, Reward: 0.9895374500855947, Total Reward: 74.56735646983952, Score: 0.028836392381134678, Total Score: 0.9365647264380629

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Step: 52, Reward: 0.960900590552957, Total Reward: 75.52825706039248, Score: 0.029140437692577098, Total Score: 0.96570516413064

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Step: 53, Reward: 0.934898411966141, Total Reward: 76.46315547235862, Score: 0.029435684041560255, Total Score: 0.9951408481722003

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Step: 54, Reward: 0.9399328645284916, Total Reward: 77.40308833688711, Score: 0.029722940960243506, Total Score: 1.0248637891324437

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Step: 55, Reward: 0.9360239412600334, Total Reward: 78.33911227814714, Score: 0.030011744764996736, Total Score: 1.0548755338974405

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Step: 56, Reward: 0.965454454056784, Total Reward: 79.30456673220392, Score: 0.0302993475138501, Total Score: 1.0851748814112905

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Step: 57, Reward: 0.9718332611847573, Total Reward: 80.27639999338868, Score: 0.030595993083092347, Total Score: 1.1157708744943828

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Step: 58, Reward: 0.9845372594852125, Total Reward: 81.26093725287389, Score: 0.030894598604835323, Total Score: 1.146665473099218

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Step: 59, Reward: 0.9294392657576154, Total Reward: 82.1903765186315, Score: 0.031197107557539388, Total Score: 1.1778625806567575

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Step: 60, Reward: 0.8527246579784103, Total Reward: 83.04310117660991, Score: 0.031482687098768114, Total Score: 1.2093452677555256

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Step: 61, Reward: 0.8159099155858793, Total Reward: 83.85901109219579, Score: 0.031744695306933704, Total Score: 1.2410899630624594

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Step: 62, Reward: 0.741569097121501, Total Reward: 84.60058018931728, Score: 0.03199539181606714, Total Score: 1.2730853548785266

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Step: 63, Reward: 0.7086527176231232, Total Reward: 85.30923290694041, Score: 0.03222324636272516, Total Score: 1.3053086012412518

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Step: 64, Reward: 0.6771859704978528, Total Reward: 85.98641887743827, Score: 0.03244098702146991, Total Score: 1.3377495882627217

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Step: 65, Reward: 0.6582119187599296, Total Reward: 86.6446307961982, Score: 0.032649059206394944, Total Score: 1.3703986474691168

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Step: 66, Reward: 0.7254782825315979, Total Reward: 87.3701090787298, Score: 0.0328513014235209, Total Score: 1.4032499488926378

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Step: 67, Reward: 0.823284930294071, Total Reward: 88.19339400902386, Score: 0.03307421190592654, Total Score: 1.4363241607985644

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Step: 68, Reward: 0.8182504990698899, Total Reward: 89.01164450809375, Score: 0.033327174462336566, Total Score: 1.4696513352609009

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Step: 69, Reward: 0.8299839711613238, Total Reward: 89.84162847925508, Score: 0.03357859013923298, Total Score: 1.5032299254001338

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Step: 70, Reward: 0.9397779200680799, Total Reward: 90.78140639932316, Score: 0.03383361104317364, Total Score: 1.5370635364433074

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Step: 71, Reward: 0.9615393552198243, Total Reward: 91.74294575454299, Score: 0.034122367239686556, Total Score: 1.571185903682994

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Step: 72, Reward: 1.0080340018794205, Total Reward: 92.75097975642241, Score: 0.03441780985552355, Total Score: 1.6056037135385175

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Step: 73, Reward: 1.1409848317293718, Total Reward: 93.89196458815178, Score: 0.03472753841811556, Total Score: 1.640331251956633

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Step: 74, Reward: 1.2590534676832261, Total Reward: 95.15101805583501, Score: 0.03507811745732648, Total Score: 1.6754093694139596

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Step: 75, Reward: 1.3807002445735241, Total Reward: 96.53171830040853, Score: 0.03546497426974049, Total Score: 1.7108743436837002

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Step: 76, Reward: 1.5025187688076878, Total Reward: 98.03423706921622, Score: 0.035889208275066586, Total Score: 1.7467635519587668

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Step: 77, Reward: 1.6138815520878553, Total Reward: 99.64811862130408, Score: 0.036350872244399625, Total Score: 1.7831144242031665

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Step: 78, Reward: 1.7165485029744698, Total Reward: 101.36466712427855, Score: 0.03684675354640925, Total Score: 1.8199611777495757

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Step: 79, Reward: 1.8184694262333714, Total Reward: 103.18313655051193, Score: 0.037374180299330775, Total Score: 1.8573353580489065

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Step: 80, Reward: 1.8574644990299636, Total Reward: 105.04060104954189, Score: 0.03793292327868928, Total Score: 1.8952682813275958

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Step: 81, Reward: 1.8645829089491746, Total Reward: 106.90518395849107, Score: 0.038503647885475965, Total Score: 1.9337719292130717

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Step: 82, Reward: 1.8941410550971716, Total Reward: 108.79932501358823, Score: 0.03907655969514282, Total Score: 1.9728484889082145

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Step: 83, Reward: 1.8502530264855113, Total Reward: 110.64957804007375, Score: 0.039658553541826506, Total Score: 2.012507042450041

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Step: 84, Reward: 1.4546757588027448, Total Reward: 112.1042537988765, Score: 0.040227062351307546, Total Score: 2.0527341048013485

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Step: 85, Reward: 1.2088353186704868, Total Reward: 113.31308911754698, Score: 0.04067402607571703, Total Score: 2.093408130877066

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Step: 86, Reward: 1.0966750044720495, Total Reward: 114.40976412201903, Score: 0.04104545285791306, Total Score: 2.1344535837349787

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Step: 87, Reward: 1.043542650360527, Total Reward: 115.45330677237955, Score: 0.04138241725805474, Total Score: 2.1758360009930335

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Step: 88, Reward: 1.0156134737161555, Total Reward: 116.4689202460957, Score: 0.04170305620921811, Total Score: 2.2175390572022518

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Step: 89, Reward: 1.0140850969394835, Total Reward: 117.48300534303519, Score: 0.04201511364059115, Total Score: 2.259554170842843

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Step: 90, Reward: 1.0251360576856314, Total Reward: 118.50814140072082, Score: 0.042326701462862465, Total Score: 2.3018808723057056

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Step: 91, Reward: 1.0343617856501035, Total Reward: 119.54250318637092, Score: 0.04264168480371826, Total Score: 2.344522557109424

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Step: 92, Reward: 1.0457811860341049, Total Reward: 120.58828437240503, Score: 0.04295950284207034, Total Score: 2.3874820599514943

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Step: 93, Reward: 1.0595894780069643, Total Reward: 121.647873850412, Score: 0.043280829605782875, Total Score: 2.4307628895572773

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Step: 94, Reward: 1.069293613696973, Total Reward: 122.71716746410897, Score: 0.04360639910576882, Total Score: 2.474369288663046

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Step: 95, Reward: 1.0759698500937362, Total Reward: 123.79313731420271, Score: 0.0439349502988255, Total Score: 2.5183042389618717

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Step: 96, Reward: 1.3074041389557671, Total Reward: 125.10054145315847, Score: 0.044265552832510414, Total Score: 2.562569791794382

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Step: 97, Reward: 1.6721521590810136, Total Reward: 126.77269361223948, Score: 0.04466726587386679, Total Score: 2.607237057668249

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Step: 98, Reward: 1.75613883059031, Total Reward: 128.52883244282978, Score: 0.04518105140461443, Total Score: 2.652418109072863

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Step: 99, Reward: 1.7563279535050194, Total Reward: 130.2851603963348, Score: 0.045720642682980664, Total Score: 2.698138751755844

[ ]