code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
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# Script to dump TensorFlow weights in TRT v1 and v2 dump format.
# The V1 format is for TensorRT 4.0. The V2 format is for TensorRT 4.0 and later.
import sys
import struct
import argparse
try:
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
except ImportError as err:
sys.stderr.w... | /rllte_core-0.0.1b1.tar.gz/rllte_core-0.0.1b1/deployment/c++/common/dumpTFWts.py | 0.466603 | 0.539529 | dumpTFWts.py | pypi |
import pandas as pd
import requests
from loguru import logger as log
from prometheus_client import Summary
HTTP_REQUEST_TIME = Summary(
"rlm_request_response_seconds", "time spent waiting for RLM to return data"
)
HTML_PARSING_TIME = Summary(
"rlm_parse_data_seconds", "time spent parsing data from the RLM r... | /rlm_prometheus-0.3.2.tar.gz/rlm_prometheus-0.3.2/src/rlm_prometheus/collector.py | 0.900253 | 0.347011 | collector.py | pypi |
from typing import Sequence, Tuple
import rdkit
from examples.redox.models import expand_inputs
from examples.redox.models import preprocessor as redox_preprocessor
from examples.redox.models import redox_model, stability_model
from examples.redox.radical_builder import build_radicals
from graphenv.vertex import V
fro... | /rlmolecule-0.0.8.tar.gz/rlmolecule-0.0.8/examples/redox/radical_state.py | 0.878118 | 0.558447 | radical_state.py | pypi |
import os
from pathlib import Path
from typing import Dict
import ray
import rdkit
from graphenv.graph_env import GraphEnv
from ray import tune
from ray.rllib.utils.framework import try_import_tf
from ray.tune.registry import register_env
from rlmolecule.builder import MoleculeBuilder
from rlmolecule.examples.qed i... | /rlmolecule-0.0.8.tar.gz/rlmolecule-0.0.8/examples/benchmarks/run_qed_aws.py | 0.753648 | 0.325976 | run_qed_aws.py | pypi |
import os
from pathlib import Path
from typing import Dict
import ray
import rdkit
from graphenv.graph_env import GraphEnv
from ray import tune
from ray.rllib.utils.framework import try_import_tf
from ray.tune.registry import register_env
from rlmolecule.builder import MoleculeBuilder
from rlmolecule.examples.qed impo... | /rlmolecule-0.0.8.tar.gz/rlmolecule-0.0.8/examples/benchmarks/qed/run_qed.py | 0.814643 | 0.309154 | run_qed.py | pypi |
import os
from pathlib import Path
from typing import Dict
import ray
import rdkit
from graphenv.graph_env import GraphEnv
from ray import tune
from ray.rllib.utils.framework import try_import_tf
from ray.tune.registry import register_env
from rlmolecule.builder import MoleculeBuilder
from rlmolecule.examples.qed i... | /rlmolecule-0.0.8.tar.gz/rlmolecule-0.0.8/examples/benchmarks/qed/run_qed_aws.py | 0.753648 | 0.325976 | run_qed_aws.py | pypi |
"""Utils module for rlog_generator."""
import datetime
import logging
import random
import socket
import struct
import sys
import yaml
log = logging.getLogger(__name__)
def load_config(yaml_file):
"""Return a Python object given a YAML file
Arguments:
yaml_file {str} -- path of YAML file
Re... | /rlog-generator-0.2.0.tar.gz/rlog-generator-0.2.0/rlog_generator/utils.py | 0.754463 | 0.346818 | utils.py | pypi |
from collections import Iterable, Sequence
from .codec import consume_length_prefix, consume_payload
from .exceptions import DecodingError
from .atomic import Atomic
def decode_lazy(rlp, sedes=None, **sedes_kwargs):
"""Decode an RLP encoded object in a lazy fashion.
If the encoded object is a bytestring, th... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp/lazy.py | 0.895831 | 0.558628 | lazy.py | pypi |
class RLPException(Exception):
"""Base class for exceptions raised by this package."""
pass
class EncodingError(RLPException):
"""Exception raised if encoding fails.
:ivar obj: the object that could not be encoded
"""
def __init__(self, message, obj):
super(EncodingError, self).__ini... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp/exceptions.py | 0.931267 | 0.342737 | exceptions.py | pypi |
from eth_utils import (
int_to_big_endian,
big_endian_to_int,
)
from rlp_cython.exceptions import DeserializationError, SerializationError
class BigEndianInt(object):
"""A sedes for big endian integers.
:param l: the size of the serialized representation in bytes or `None` to
use the s... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp/sedes/big_endian_int.py | 0.727007 | 0.352898 | big_endian_int.py | pypi |
import abc
import collections
import copy
import enum
import re
from eth_utils import (
to_dict,
to_set,
to_tuple,
)
from rlp_cython.exceptions import (
ListSerializationError,
ObjectSerializationError,
ListDeserializationError,
ObjectDeserializationError,
)
from .lists import (
List,... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp/sedes/serializable.py | 0.596903 | 0.151529 | serializable.py | pypi |
from rlp_cython.exceptions import SerializationError, DeserializationError
from rlp_cython.atomic import Atomic
class Text:
"""A sedes object for encoded text data of certain length.
:param min_length: the minimal length in encoded characters or `None` for no lower limit
:param max_length: the maximal le... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp/sedes/text.py | 0.858244 | 0.314787 | text.py | pypi |
Tutorial
========
Basics
------
There are two types of fundamental items one can encode in RLP:
1) Strings of bytes
2) Lists of other items
In this package, byte strings are represented either as Python strings or as
``bytearrays``. Lists can be any sequence, e.g. ``lists`` or ``tuples``. To
encode these ki... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/docs/tutorial.rst | 0.942062 | 0.718557 | tutorial.rst | pypi |
from collections import Iterable, Sequence
from .codec import consume_length_prefix, consume_payload
from .exceptions import DecodingError
from .atomic import Atomic
def decode_lazy(rlp, sedes=None, **sedes_kwargs):
"""Decode an RLP encoded object in a lazy fashion.
If the encoded object is a bytestring, th... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp_cython/lazy.py | 0.895831 | 0.558628 | lazy.py | pypi |
class RLPException(Exception):
"""Base class for exceptions raised by this package."""
pass
class EncodingError(RLPException):
"""Exception raised if encoding fails.
:ivar obj: the object that could not be encoded
"""
def __init__(self, message, obj):
super(EncodingError, self).__ini... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp_cython/exceptions.py | 0.931267 | 0.342737 | exceptions.py | pypi |
from eth_utils import (
int_to_big_endian,
big_endian_to_int,
)
from rlp_cython.exceptions import DeserializationError, SerializationError
class BigEndianInt(object):
"""A sedes for big endian integers.
:param l: the size of the serialized representation in bytes or `None` to
use the s... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp_cython/sedes/big_endian_int.py | 0.727007 | 0.352898 | big_endian_int.py | pypi |
import abc
import collections
import copy
import enum
import re
from eth_utils import (
to_dict,
to_set,
to_tuple,
)
from rlp_cython.exceptions import (
ListSerializationError,
ObjectSerializationError,
ListDeserializationError,
ObjectDeserializationError,
)
from .lists import (
List,... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp_cython/sedes/serializable.py | 0.596903 | 0.151529 | serializable.py | pypi |
from rlp_cython.exceptions import SerializationError, DeserializationError
from rlp_cython.atomic import Atomic
class Text:
"""A sedes object for encoded text data of certain length.
:param min_length: the minimal length in encoded characters or `None` for no lower limit
:param max_length: the maximal le... | /rlp-cython-2.1.7.tar.gz/rlp-cython-2.1.7/rlp_cython/sedes/text.py | 0.858244 | 0.314787 | text.py | pypi |
from src.rlpe.constants import *
from src.rlpe.Agents.RL_agents import rl_agent
import numpy as np
# helper function for flattening irregular nested tuples
def mixed_flatten(x):
result = []
for el in x:
if hasattr(el, "__iter__"):
result.extend(mixed_flatten(el))
else:
... | /Observer/anticipated_policy_generator.py | 0.479016 | 0.523603 | anticipated_policy_generator.py | pypi |
# RLPipes
<img src="https://rlbase-data.s3.amazonaws.com/misc/assets/whitebgRLPipes+Logo.png" align="right" alt="logo" width="240" style = "border: none; float: right;">
 [ in SumTree
class PrioritizedReplayBuffer:
def __init__(self, capacity, alpha=0.6, beta=0.4, beta_increment_per_sampling=0.001, e=0.01):
self.tree = SumTree(capacity)
self.alpha = alpha # (0 - no prioritization, 1 - full prio... | /rlprop-0.0.4-py3-none-any.whl/prop/buffers/priority_replay_buffer.py | 0.831349 | 0.313512 | priority_replay_buffer.py | pypi |
.. _make_agent:
.. this is a comment. see http://sphinx-doc.org/rest.html for markup instructions
Creating a New Agent
====================
This tutorial describes the standard RLPy :class:`~rlpy.Agents.Agent.Agent` interface,
and illustrates a brief example of creating a new learning agent.
.. Below taken directly... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/doc/make_agent.rst | 0.961335 | 0.720067 | make_agent.rst | pypi |
.. _tutorial:
Getting Started
===============
This tutorial covers the most common type of experiment in reinforcement
learning: the control experiment. An agent is supposed to find a good policy
while interacting with the domain.
.. note::
If you don't use the developer verion of rlpy but installed the toolbo... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/doc/tutorial.rst | 0.945914 | 0.949106 | tutorial.rst | pypi |
.. _make_domain:
.. this is a comment. see http://sphinx-doc.org/rest.html for markup instructions
Creating a New Domain
=====================
This tutorial describes the standard RLPy
:class:`~rlpy.Domains.Domain.Domain` interface,
and illustrates a brief example of creating a new problem domain.
.. Below taken d... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/doc/make_domain.rst | 0.956695 | 0.695209 | make_domain.rst | pypi |
.. _make_rep:
.. this is a comment. see http://sphinx-doc.org/rest.html for markup instructions
Creating a New Representation
=============================
This tutorial describes the standard RLPy
:class:`~rlpy.Representations.Representation.Representation` interface,
and illustrates a brief example of creating a ... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/doc/make_rep.rst | 0.948549 | 0.698291 | make_rep.rst | pypi |
import rlpy
import numpy as np
from hyperopt import hp
param_space = {
'kernel_resolution':
hp.loguniform("kernel_resolution", np.log(5), np.log(50)),
'discover_threshold':
hp.loguniform(
"discover_threshold",
np.log(1e4),
np.log(1e8)),
'lambda_': hp.uniform("lambda_", 0., 1... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/bicycle/kifdd_triangle.py | 0.443359 | 0.375449 | kifdd_triangle.py | pypi |
from rlpy.Domains import PuddleWorld
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
param_space = {
'kernel_resolution':
hp.loguniform("kernel_resolution", np.log(3... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/puddleworld/kifdd_triangle.py | 0.504639 | 0.351311 | kifdd_triangle.py | pypi |
from rlpy.Domains.PuddleWorld import PuddleGapWorld
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy, UniformRandom
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representations import KernelizediFDD
param_spac... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/puddleworld/kifdd_gauss_gap.py | 0.529993 | 0.400808 | kifdd_gauss_gap.py | pypi |
from rlpy.Domains import PuddleWorld
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representations import KernelizediFDD
param_space = {
'kernel_resolution':... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/puddleworld/kifdd_gauss.py | 0.50415 | 0.340567 | kifdd_gauss.py | pypi |
__author__ = "William Dabney"
from rlpy.Domains import GridWorld
from rlpy.Agents import Q_Learning
from rlpy.Representations import iFDDK, IndependentDiscretization
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import os
def make_experiment(exp_id=1, path="./Results/Temp"):
"""
... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/gridworld/q-ifddk.py | 0.666062 | 0.355048 | q-ifddk.py | pypi |
from rlpy.Domains.FiniteTrackCartPole import FiniteCartPoleBalanceOriginal, FiniteCartPoleBalanceModern
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representati... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/cartpole_orig/kifdd_triangle.py | 0.482673 | 0.377426 | kifdd_triangle.py | pypi |
from rlpy.Domains.FiniteTrackCartPole import FiniteCartPoleBalanceOriginal, FiniteCartPoleBalanceModern
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
param_space = {'discr... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/cartpole_orig/ifdd.py | 0.443359 | 0.378402 | ifdd.py | pypi |
from rlpy.Domains.FiniteTrackCartPole import FiniteCartPoleBalanceOriginal, FiniteCartPoleBalanceModern
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representati... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/cartpole_orig/kifdd_gauss.py | 0.489503 | 0.374648 | kifdd_gauss.py | pypi |
from rlpy.Domains import Swimmer
from rlpy.Agents import Q_Learning, SARSA
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Policies.SwimmerPolicy import SwimmerPolicy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representations import Kernel... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/swimmer/kifdd_triangle.py | 0.537041 | 0.386242 | kifdd_triangle.py | pypi |
from rlpy.Domains import InfCartPoleBalance
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representations import KernelizediFDD
param_space = {
'kernel_resol... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/cartpole2d/kifdd_triangle.py | 0.5 | 0.331039 | kifdd_triangle.py | pypi |
from rlpy.Domains import InfCartPoleBalance
from rlpy.Agents import Greedy_GQ, SARSA, Q_Learning
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
param_space = {'discretization': hp.quniform("discretization", 5, 40, ... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/cartpole2d/ggq-ifdd.py | 0.449876 | 0.360433 | ggq-ifdd.py | pypi |
from rlpy.Domains import InfCartPoleBalance
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
param_space = {
'kernel_resolution':
hp.loguniform("kernel_resolution", n... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/cartpole2d/kifdd_gauss.py | 0.503662 | 0.343865 | kifdd_gauss.py | pypi |
from rlpy.Domains.HIVTreatment import HIVTreatment
from rlpy.Agents import Q_Learning
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representations import KernelizediFDD
param_space = {
'kernel_resol... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/hiv/kifdd_triangle.py | 0.459804 | 0.403038 | kifdd_triangle.py | pypi |
from rlpy.Domains.HIVTreatment import HIVTreatment
from rlpy.Agents import Q_Learning
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representations import KernelizediFDD
param_space = {
'kernel_resol... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/hiv/kifdd.py | 0.467818 | 0.394201 | kifdd.py | pypi |
from rlpy.Domains import HelicopterHover
from rlpy.Agents import Q_Learning
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
from rlpy.Representations import KernelizediFDD
param_space = {
'kernel_resolution':
... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/heli/kifdd.py | 0.46563 | 0.398699 | kifdd.py | pypi |
from rlpy.Domains.FiniteTrackCartPole import FiniteCartPoleBalanceOriginal, FiniteCartPoleBalanceModern
from rlpy.Agents import SARSA, Q_LEARNING
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
param_space = {
'... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/cartpole_modern/kifdd.py | 0.533154 | 0.396594 | kifdd.py | pypi |
from rlpy.Domains import BlocksWorld
from rlpy.Agents import Greedy_GQ
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
param_space = {'boyan_N0': hp.loguniform("boyan_N0", np.log(1e1), np.log(1e5)),
'... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/blocksworld/ggq-tile.py | 0.547464 | 0.444987 | ggq-tile.py | pypi |
from rlpy.Domains import PST
from rlpy.Agents import Greedy_GQ
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp
param_space = { # 'discretization': hp.quniform("discretization", 5, 50, 1),
'discover_threshold': ... | /rlpy-1.3.8.tar.gz/rlpy-1.3.8/examples/uav/gq-ifdd.py | 0.40028 | 0.311427 | gq-ifdd.py | pypi |
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import json
import os
import numpy as np
import glob
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. Ho... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Tools/results.py | 0.698535 | 0.490968 | results.py | pypi |
import click
from rlpy.Domains.Domain import Domain
from rlpy.Experiments import Experiment
def get_experiment(
domain_or_domain_selector,
agent_selector,
default_max_steps=1000,
default_num_policy_checks=10,
default_checks_per_policy=10,
other_options=[],
):
@click.group()
@click.opti... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Tools/cli.py | 0.553505 | 0.199815 | cli.py | pypi |
from .MDPSolver import MDPSolver
from rlpy.Tools import hhmmss, deltaT, className, clock, l_norm
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__licen... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/MDPSolvers/ValueIteration.py | 0.858807 | 0.512998 | ValueIteration.py | pypi |
from .MDPSolver import MDPSolver
from rlpy.Tools import className, deltaT, hhmmss, clock, l_norm
from copy import deepcopy
from rlpy.Policies import eGreedy
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph D... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/MDPSolvers/PolicyIteration.py | 0.860574 | 0.396682 | PolicyIteration.py | pypi |
from .MDPSolver import MDPSolver
from rlpy.Tools import deltaT, hhmmss, randSet, className, clock
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__lice... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/MDPSolvers/TrajectoryBasedValueIteration.py | 0.805632 | 0.546194 | TrajectoryBasedValueIteration.py | pypi |
from abc import ABC, abstractmethod
import numpy as np
import logging
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__ = "Alborz Ger... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Agents/Agent.py | 0.925217 | 0.484624 | Agent.py | pypi |
import numpy as np
from .Agent import Agent
from rlpy.Tools import solveLinear, regularize
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__a... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Agents/NaturalActorCritic.py | 0.781205 | 0.522568 | NaturalActorCritic.py | pypi |
from .LSPI import LSPI
from .TDControlAgent import SARSA
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__ = "Alborz Geramifard"
# E... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Agents/LSPI_SARSA.py | 0.649023 | 0.213254 | LSPI_SARSA.py | pypi |
from .Agent import Agent
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__ = "Alborz Geramifard"
class BatchAgen... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Agents/BatchAgent.py | 0.779028 | 0.297095 | BatchAgent.py | pypi |
from .Agent import Agent, DescentAlgorithm
from rlpy.Tools import addNewElementForAllActions, count_nonzero
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How"... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Agents/TDControlAgent.py | 0.68784 | 0.365513 | TDControlAgent.py | pypi |
import numpy as np
import logging
from copy import deepcopy
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
class Domain(object):
"""
... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/Domain.py | 0.8398 | 0.627951 | Domain.py | pypi |
from .Domain import Domain
import numpy as np
from itertools import tee
import itertools
import os
try:
from tkinter import Tk, Canvas
except ImportError:
import warnings
warnings.warn("TkInter is not found for Pinball.")
from rlpy.Tools import __rlpy_location__
__copyright__ = "Copyright 2013, RLPy http... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/Pinball.py | 0.784979 | 0.359027 | Pinball.py | pypi |
import numpy as np
import itertools
from rlpy.Tools import plt, FONTSIZE, linearMap
from rlpy.Tools import __rlpy_location__, findElemArray1D, perms
import os
from .Domain import Domain
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/GridWorld.py | 0.673084 | 0.415966 | GridWorld.py | pypi |
from rlpy.Tools import FONTSIZE, id2vec, plt
from .Domain import Domain
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__a... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/FlipBoard.py | 0.595022 | 0.588268 | FlipBoard.py | pypi |
from .Domain import Domain
import numpy as np
from scipy.integrate import odeint
from rlpy.Tools import plt
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/HIVTreatment.py | 0.59561 | 0.571587 | HIVTreatment.py | pypi |
from rlpy.Tools import plt, mpatches, fromAtoB
from .Domain import Domain
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
_... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/ChainMDP.py | 0.695441 | 0.520435 | ChainMDP.py | pypi |
from rlpy.Tools import plt, id2vec, bound_vec
import numpy as np
from .Domain import Domain
import os
from rlpy.Tools import __rlpy_location__, FONTSIZE
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabn... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/IntruderMonitoring.py | 0.600423 | 0.437643 | IntruderMonitoring.py | pypi |
import itertools
import numpy as np
from rlpy.Tools import __rlpy_location__, plt, with_bold_fonts
import os
from .GridWorld import GridWorld
__license__ = "BSD 3-Clause"
__author__ = "Yuji Kanagawa"
class AnyRewardGridWorld(GridWorld):
"""The same as GridWorld, but you can set any reward for each cell.
"""... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/any_reward_grid_world.py | 0.452294 | 0.237272 | any_reward_grid_world.py | pypi |
from .Domain import Domain
import numpy as np
from rlpy.Tools import mpl, plt, rk4, cartesian, colors
from rlpy.Policies.SwimmerPolicy import SwimmerPolicy
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William ... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/Swimmer.py | 0.800536 | 0.561095 | Swimmer.py | pypi |
from .Domain import Domain
import numpy as np
import matplotlib.pyplot as plt
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__ = "Ch... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/PuddleWorld.py | 0.660063 | 0.586996 | PuddleWorld.py | pypi |
from .Domain import Domain
import numpy as np
from itertools import product
from rlpy.Tools import plt
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/Bicycle.py | 0.741861 | 0.543227 | Bicycle.py | pypi |
from .CartPoleBase import CartPoleBase, StateIndex
import numpy as np
from rlpy.Tools import pl, plt
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-C... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/FiniteTrackCartPole.py | 0.794146 | 0.658459 | FiniteTrackCartPole.py | pypi |
from rlpy.Tools import plt, bound, fromAtoB
from rlpy.Tools import lines
from .Domain import Domain
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__li... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/MountainCar.py | 0.84137 | 0.62701 | MountainCar.py | pypi |
from rlpy.Tools import plt, mpatches
import numpy as np
from .Domain import Domain
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__ ... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/FiftyChain.py | 0.760028 | 0.545528 | FiftyChain.py | pypi |
from .game import Agent
from .game import Actions
from .game import Directions
from .util import manhattanDistance
from . import util
class GhostAgent(Agent):
def __init__(self, index):
self.index = index
def getAction(self, state):
dist = self.getDistribution(state)
if len(dist) == 0... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/PacmanPackage/ghostAgents.py | 0.741674 | 0.350157 | ghostAgents.py | pypi |
from .util import manhattanDistance
from .game import Grid
import os
import random
from functools import reduce
VISIBILITY_MATRIX_CACHE = {}
class Layout(object):
"""
A Layout manages the static information about the game board.
"""
def __init__(self, layoutText):
self.width = len(layoutTex... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Domains/PacmanPackage/layout.py | 0.607081 | 0.261798 | layout.py | pypi |
from rlpy.Tools import className, discrete_sample
import numpy as np
import logging
from abc import ABC, abstractmethod
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__l... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Policies/Policy.py | 0.86129 | 0.589716 | Policy.py | pypi |
from .Policy import Policy
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__ = "Alborz Geramifard"
class eGreedy... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Policies/eGreedy.py | 0.858452 | 0.497559 | eGreedy.py | pypi |
from rlpy.Tools import perms
from .Representation import Representation
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__a... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Representations/RBF.py | 0.806243 | 0.510008 | RBF.py | pypi |
from .Representation import Representation
import numpy as np
from rlpy.Tools.GeneralTools import addNewElementForAllActions
import matplotlib.pyplot as plt
try:
from .kernels import batch
except ImportError:
from .slow_kernels import batch
print("C-Extensions for kernels not available, expect slow runtim... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Representations/LocalBases.py | 0.733833 | 0.437223 | LocalBases.py | pypi |
from .Representation import Representation
import numpy as np
from copy import copy
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Representations/IndependentDiscretizationCompactBinary.py | 0.787114 | 0.540985 | IndependentDiscretizationCompactBinary.py | pypi |
from .Representation import Representation
import numpy as np
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__author__ = "Alborz Geramifard"... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Representations/IndependentDiscretization.py | 0.812756 | 0.444625 | IndependentDiscretization.py | pypi |
from .Representation import Representation
import numpy as np
from .iFDD import iFDD
from rlpy.Tools import className, plt
from copy import deepcopy
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Representations/OMPTD.py | 0.770033 | 0.465813 | OMPTD.py | pypi |
from .Representation import Representation
import numpy as np
from copy import deepcopy
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonathan P. How",
]
__license__ = "BSD 3-Clause"
__auth... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Representations/IncrementalTabular.py | 0.730386 | 0.340047 | IncrementalTabular.py | pypi |
import numpy as np
from .Representation import Representation
from itertools import combinations
from rlpy.Tools import addNewElementForAllActions, PriorityQueueWithNovelty
import matplotlib.pyplot as plt
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Rober... | /rlpy3-2.0.0a0-cp36-cp36m-win_amd64.whl/rlpy/Representations/KernelizediFDD.py | 0.616012 | 0.346431 | KernelizediFDD.py | pypi |
import itertools
import numpy as np
class RLSA(object):
"""
Class that contains the logic to apply the Run Length Smoothing Algorithm (RLSA) on an image.
"""
@staticmethod
def apply_rlsa(img, h_threshold=0, v_threshold=0, hf_threshold=0):
"""
Method that applies the 'smear_line' ... | /rlsa_python-0.1.tar.gz/rlsa_python-0.1/rlsa_python/rlsa.py | 0.924811 | 0.622861 | rlsa.py | pypi |
=======
RLScore
=======
RLScore - regularized least-squares machine learning algorithms package.
:Authors: `Tapio Pahikkala <http://staff.cs.utu.fi/~aatapa/>`_,
`Antti Airola <https://scholar.google.fi/citations?user=5CPOSr0AAAAJ>`_
:Email: firstname.lastname@utu.fi
:Homepage: ... | /rlscore-0.8.1.tar.gz/rlscore-0.8.1/README.rst | 0.950881 | 0.865167 | README.rst | pypi |
from gym.spaces import Dict
from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.torch_utils import FLOAT_MIN
tf1, tf, tfv = try_import_tf()
tor... | /models/action_mask_model.py | 0.917437 | 0.473109 | action_mask_model.py | pypi |
import glob
import os
from typing import Tuple
import ray.tune
from ray import init
from ray.rllib.agents import ppo
from ray.rllib.env import PettingZooEnv
from ray.rllib.models import ModelCatalog
from ray.rllib.utils.framework import try_import_torch
from ray.tune.logger import pretty_print
from ray.tune.registry i... | /models/train_model_simple_rllib.py | 0.79909 | 0.300566 | train_model_simple_rllib.py | pypi |
import math
from enum import IntEnum
from typing import Any
import torch
from tensordict import TensorDict
from torchrl.data import (
CompositeSpec,
DiscreteTensorSpec,
UnboundedContinuousTensorSpec,
)
from rlstack import Env
from rlstack.data import DataKeys, Device
class Action(IntEnum):
"""Enumer... | /rlstack-0.1.2.tar.gz/rlstack-0.1.2/examples/algotrading/env.py | 0.893046 | 0.602997 | env.py | pypi |
import torch
import torch.nn as nn
from tensordict import TensorDict
from torchrl.data import CompositeSpec, TensorSpec, UnboundedContinuousTensorSpec
from rlstack import RecurrentModel
from rlstack.data import DataKeys
from rlstack.nn import MLP, get_activation
FINFO = torch.finfo()
class LazyLemur(RecurrentModel)... | /rlstack-0.1.2.tar.gz/rlstack-0.1.2/examples/algotrading/models/lstm.py | 0.931509 | 0.702313 | lstm.py | pypi |
import torch
import torch.nn as nn
from tensordict import TensorDict
from torchrl.data import TensorSpec
from rlstack import Model
from rlstack.data import DataKeys
from rlstack.nn import (
MLP,
SelfAttention,
SelfAttentionStack,
get_activation,
masked_avg,
)
from rlstack.views import ViewRequireme... | /rlstack-0.1.2.tar.gz/rlstack-0.1.2/examples/algotrading/models/transformer.py | 0.931572 | 0.757324 | transformer.py | pypi |
import torch
import torch.nn as nn
from tensordict import TensorDict
from torchrl.data import TensorSpec
from rlstack import Model
from rlstack.data import DataKeys
from rlstack.nn import MLP, get_activation
from rlstack.views import ViewRequirement
FINFO = torch.finfo()
class MischievousMule(Model):
"""A model... | /rlstack-0.1.2.tar.gz/rlstack-0.1.2/examples/algotrading/models/mlp.py | 0.921411 | 0.745584 | mlp.py | pypi |
from ibapi import order_condition
from ibapi.object_implem import Object
from ibapi.utils import * # @UnusedWildImport
from ibapi.server_versions import * # @UnusedWildImport
from ibapi.order import OrderComboLeg
from ibapi.contract import ComboLeg
from ibapi.tag_value import TagValue
from ibapi.wrapper import DeltaNeu... | /rltrade-ibapi-9.76.1.tar.gz/rltrade-ibapi-9.76.1/ibapi/orderdecoder.py | 0.581065 | 0.151969 | orderdecoder.py | pypi |
This is the interface that will need to be overloaded by the customer so
that his/her code can receive info from the TWS/IBGW.
NOTE: the methods use type annotations to describe the types of the arguments.
This is used by the Decoder to dynamically and automatically decode the
received message into the given EWrapper ... | /rltrade-ibapi-9.76.1.tar.gz/rltrade-ibapi-9.76.1/ibapi/wrapper.py | 0.652352 | 0.459137 | wrapper.py | pypi |
from toyrobot.models.orientation import Orientation
from toyrobot.models.robot import Robot
import logging
class CommandParser:
def __init__(self):
self.valid_commands = ["PLACE", "LEFT", "RIGHT", "MOVE", "REPORT"]
def _parse_place_cmd_string(self, cmd):
return cmd[1].strip().split(",")
... | /rlupat.toyrobot-0.2.1.tar.gz/rlupat.toyrobot-0.2.1/toyrobot/parser/commandparser.py | 0.682468 | 0.309689 | commandparser.py | pypi |
import json
import os
import os.path as osp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
DIV_LINE_WIDTH = 50
# Global vars for tracking and labeling data at load time.
exp_idx = 0
units = dict()
def smooth_dataframe(dataframe, value, smooth):
y = np.ones(smooth)
... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/plot.py | 0.479747 | 0.453141 | plot.py | pypi |
import atexit
import json
import os
import os.path as osp
import shutil
import time
import numpy as np
from rlutils.utils.serialization_utils import convert_json
from tensorboardX import SummaryWriter
DEFAULT_DATA_DIR = 'data'
FORCE_DATESTAMP = False
color2num = dict(
gray=30,
red=31,
green=32,
yello... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/logx.py | 0.733356 | 0.352648 | logx.py | pypi |
import torch.nn as nn
from .layers import EnsembleDense, SqueezeLayer
str_to_activation = {
'relu': nn.ReLU,
'leaky_relu': nn.LeakyReLU,
'tanh': nn.Tanh,
'sigmoid': nn.Sigmoid,
'softplus': nn.Softplus,
}
def decode_activation(activation):
if isinstance(activation, str):
act_fn = str_... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/pytorch/nn/functional.py | 0.837155 | 0.33689 | functional.py | pypi |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from rlutils.np.functional import inverse_softplus
class EnsembleDense(nn.Module):
__constants__ = ['num_ensembles', 'in_features', 'out_features']
in_features: int
out_features: int
weight: torch.Tensor
def __init__(... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/pytorch/nn/layers.py | 0.920843 | 0.424412 | layers.py | pypi |
import copy
import rlutils.pytorch.utils as ptu
import torch
import torch.nn as nn
from rlutils.infra.runner import run_func_as_main, PytorchOffPolicyRunner
from rlutils.pytorch.functional import soft_update, compute_target_value, to_numpy_or_python_type
from rlutils.pytorch.nn import EnsembleMinQNet
from rlutils.pyto... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/pytorch/mf/td3.py | 0.882504 | 0.36727 | td3.py | pypi |
import copy
import rlutils.pytorch as rlu
import rlutils.pytorch.utils as ptu
import torch
from rlutils.infra.runner import PytorchOffPolicyRunner, run_func_as_main
from torch import nn
class SACAgent(nn.Module):
def __init__(self,
obs_spec,
act_spec,
num_ensemb... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/pytorch/mf/sac.py | 0.893765 | 0.368093 | sac.py | pypi |
import numpy as np
import rlutils.tf as rlu
import tensorflow as tf
from rlutils.infra.runner import TFOnPolicyRunner
class PPOAgent(tf.keras.Model):
def __init__(self, obs_spec, act_spec, mlp_hidden=64,
pi_lr=1e-3, vf_lr=1e-3, clip_ratio=0.2,
entropy_coef=0.001, target_kl=0.05,
... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/mf/ppo.py | 0.883488 | 0.393968 | ppo.py | pypi |
import numpy as np
import rlutils.tf as rlu
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.infra.runner import TFOnPolicyRunner
class TRPOAgent(tf.keras.Model):
def __init__(self, obs_spec, act_spec, mlp_hidden=64,
delta=0.01, vf_lr=1e-3, damping_coeff=0.1, cg_iters=10,... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/mf/trpo.py | 0.888638 | 0.44734 | trpo.py | pypi |
import rlutils.tf as rlu
import tensorflow as tf
from rlutils.infra.runner import TFOffPolicyRunner
class TD3Agent(tf.keras.Model):
def __init__(self,
obs_spec,
act_spec,
num_q_ensembles=2,
policy_mlp_hidden=256,
policy_lr=3e-4,
... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/mf/td3.py | 0.792544 | 0.236021 | td3.py | pypi |
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