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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"""An online Q-lambda agent trained to play BSuite's Catch env."""
import collections
from absl import app
from absl import flags
from bsuite.environments import catch
import dm_env
import haiku as hk
from haiku import nets
import jax
import jax.numpy as jnp
import numpy as np
import optax
import rlax
from rlax.exampl... | /rlax-0.1.6-py3-none-any.whl/examples/online_q_lambda.py | 0.885291 | 0.452475 | online_q_lambda.py | pypi |
"""A simple online Q-learning agent trained to play BSuite's Catch env."""
import collections
from absl import app
from absl import flags
from bsuite.environments import catch
import haiku as hk
from haiku import nets
import jax
import jax.numpy as jnp
import optax
import rlax
from rlax.examples import experiment
Act... | /rlax-0.1.6-py3-none-any.whl/examples/online_q_learning.py | 0.886402 | 0.53206 | online_q_learning.py | pypi |
import math
import numpy as np
import rlbase.misc as misc
class BaseAgent:
def __init__(self,**kwargs):
self.pi = kwargs.get("pi")
env = kwargs.get("env")
q_init = kwargs.get("q_init",0)
self.v = {s:0 for s in env.states}
self.nv = {s:0 for s in env.states}
self.q = {s:{a:q_init for a in env... | /rlbase-chicotobi-0.6.0.tar.gz/rlbase-chicotobi-0.6.0/src/rlbase/agent.py | 0.500977 | 0.24646 | agent.py | pypi |
import numpy as np
import numpy.random as npr
import rlbase.misc as misc
def transform_Q_to_BestAction(Q):
return {s:misc.all_argmax(Q[s]) for s in Q.keys()}
class Policy:
def __init__(self,**kwargs):
self.valid_actions = kwargs.get("env").valid_actions
self.n_valid_actions = {s:len(a) for (s,a) in self.... | /rlbase-chicotobi-0.6.0.tar.gz/rlbase-chicotobi-0.6.0/src/rlbase/policy.py | 0.449634 | 0.230974 | policy.py | pypi |
import typing as t
import attrs
import numpy as np
import numpy.typing as npt
from rlbcore import api, uis
from rlbcore.external_utils.other import null_object
@attrs.define()
class EpisodeReturnRecorder:
"""Records episode returns during training and prints them to console.
Args:
ui (CliUI): The U... | /external_utils/gym_.py | 0.869452 | 0.804137 | gym_.py | pypi |
import functools
import typing as t
from pprint import pformat
from omegaconf import MISSING, DictConfig, ListConfig, MissingMandatoryValue, OmegaConf
def nested_dict_contains_dot_key(
dictionary: dict[str, t.Any] | DictConfig, dot_key: str
) -> bool:
"""Return True if the dictionary contains dot_key in the ... | /external_utils/builtins_.py | 0.792986 | 0.712507 | builtins_.py | pypi |
import typing as t
from unittest.mock import MagicMock
AnyT = t.TypeVar("AnyT")
def null_object(
cls: type[AnyT],
property_returns: dict[str, t.Any] | None = None,
method_returns: dict[str, t.Any] | None = None,
) -> AnyT:
"""Create a null object following the Null object pattern.
Args:
... | /external_utils/other.py | 0.852506 | 0.759359 | other.py | pypi |
<!-- Logo -->
<p align="center">
<img src="https://raw.githubusercontent.com/rlberry-py/rlberry/main/assets/logo_wide.svg" width="50%">
</p>
<!-- Short description -->
<p align="center">
A Reinforcement Learning Library for Research and Education
</p>
<!-- The badges -->
<p align="center">
<a href="https:... | /rlberry-0.5.0.tar.gz/rlberry-0.5.0/README.md | 0.418697 | 0.8308 | README.md | pypi |
import datetime
from typing import TypeVar
import six
from rlbot_action_server import typing_utils
def _deserialize(data, klass):
"""Deserializes dict, list, str into an object.
:param data: dict, list or str.
:param klass: class literal, or string of class name.
:return: object.
"""
if dat... | /rlbot_action_server-1.1.0.tar.gz/rlbot_action_server-1.1.0/rlbot_action_server/util.py | 0.797675 | 0.376222 | util.py | pypi |
from math import pi
from typing import List
import eel
from rlbot.gateway_util import NetworkingRole
from rlbot.matchconfig.loadout_config import LoadoutConfig
from rlbot.matchconfig.match_config import PlayerConfig, MatchConfig, MutatorConfig, ScriptConfig
from rlbot.parsing.incrementing_integer import IncrementingIn... | /rlbot_gui-0.0.140-py3-none-any.whl/rlbot_gui/match_runner/match_runner.py | 0.545044 | 0.235534 | match_runner.py | pypi |
from contextlib import contextmanager
from datetime import datetime
from os import path
from typing import List, Optional
import glob
import shutil
import os
from rlbot.setup_manager import (
SetupManager,
RocketLeagueLauncherPreference,
try_get_steam_executable_path,
)
from rlbot.gamelaunch.epic_launch ... | /rlbot_gui-0.0.140-py3-none-any.whl/rlbot_gui/match_runner/custom_maps.py | 0.54698 | 0.201145 | custom_maps.py | pypi |
import platform
import random
import time
from datetime import datetime
from multiprocessing import Queue as MPQueue
from traceback import print_exc
from typing import Tuple
from rlbot.matchconfig.match_config import MatchConfig, MutatorConfig
from rlbot.parsing.match_settings_config_parser import (game_mode_types,
... | /rlbot_smh-1.0.13.tar.gz/rlbot_smh-1.0.13/src/rlbot_smh/story_mode_util.py | 0.521227 | 0.21713 | story_mode_util.py | pypi |
from math import pi
from rlbot.gateway_util import NetworkingRole
from rlbot.matchconfig.loadout_config import LoadoutConfig
from rlbot.matchconfig.match_config import (MatchConfig, MutatorConfig,
PlayerConfig)
from rlbot.parsing.agent_config_parser import (
BOT_CONFIG_L... | /rlbot_smh-1.0.13.tar.gz/rlbot_smh-1.0.13/src/rlbot_smh/showroom_util.py | 0.521959 | 0.282042 | showroom_util.py | pypi |
import datetime
from typing import TypeVar
import six
from rlbot_action_server import typing_utils
def _deserialize(data, klass):
"""Deserializes dict, list, str into an object.
:param data: dict, list or str.
:param klass: class literal, or string of class name.
:return: object.
"""
if dat... | /rlbot_twitch_broker_server-1.0.0.tar.gz/rlbot_twitch_broker_server-1.0.0/rlbot_twitch_broker_server/util.py | 0.797675 | 0.376222 | util.py | pypi |
# RLCard: A Toolkit for Reinforcement Learning in Card Games
<img width="500" src="https://dczha.com/files/rlcard/logo.jpg" alt="Logo" />
[](https://github.com/datamllab/rlcard/actions/workflows/python-package.yml)
[![PyPI ve... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/README.md | 0.530966 | 0.953449 | README.md | pypi |
import importlib
class ModelSpec(object):
''' A specification for a particular Model.
'''
def __init__(self, model_id, entry_point=None):
''' Initilize
Args:
model_id (string): the name of the model
entry_point (string): a string that indicates the location of the m... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/models/registration.py | 0.78016 | 0.273406 | registration.py | pypi |
import numpy as np
import rlcard
from rlcard.models.model import Model
class UNORuleAgentV1(object):
''' UNO Rule agent version 1
'''
def __init__(self):
self.use_raw = True
def step(self, state):
''' Predict the action given raw state. A naive rule. Choose the color
that... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/models/uno_rule_models.py | 0.812123 | 0.358353 | uno_rule_models.py | pypi |
from typing import TYPE_CHECKING
from collections import OrderedDict
if TYPE_CHECKING:
from rlcard.core import Card
from typing import List
import numpy as np
import rlcard
from rlcard.models.model import Model
from rlcard.games.gin_rummy.utils.action_event import *
import rlcard.games.gin_rummy.utils.meldin... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/models/gin_rummy_rule_models.py | 0.666714 | 0.346154 | gin_rummy_rule_models.py | pypi |
import numpy as np
import rlcard
from rlcard.games.doudizhu.utils import CARD_TYPE, INDEX
from rlcard.models.model import Model
class DouDizhuRuleAgentV1(object):
''' Dou Dizhu Rule agent version 1
'''
def __init__(self):
self.use_raw = True
def step(self, state):
''' Predict the act... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/models/doudizhu_rule_models.py | 0.44553 | 0.307189 | doudizhu_rule_models.py | pypi |
import hashlib
import numpy as np
import os
import struct
def colorize(string, color, bold=False, highlight = False):
"""Return string surrounded by appropriate terminal color codes to
print colorized text. Valid colors: gray, red, green, yellow,
blue, magenta, cyan, white, crimson
"""
attr = []
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/utils/seeding.py | 0.643329 | 0.306203 | seeding.py | pypi |
import numpy as np
from rlcard.games.base import Card
def set_seed(seed):
if seed is not None:
import subprocess
import sys
reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'])
installed_packages = [r.decode().split('==')[0] for r in reqs.split()]
if 'to... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/utils/utils.py | 0.578686 | 0.327507 | utils.py | pypi |
from collections import defaultdict
import numpy as np
def wrap_state(state):
# check if obs is already wrapped
if "obs" in state and "legal_actions" in state and "raw_legal_actions" in state:
return state
wrapped_state = {}
wrapped_state["obs"] = state["observation"]
legal_actions = np.f... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/utils/pettingzoo_utils.py | 0.593374 | 0.307267 | pettingzoo_utils.py | pypi |
import numpy as np
from copy import deepcopy
from rlcard.games.mahjong import Dealer
from rlcard.games.mahjong import Player
from rlcard.games.mahjong import Round
from rlcard.games.mahjong import Judger
class MahjongGame:
def __init__(self, allow_step_back=False):
'''Initialize the class MajongGame
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/mahjong/game.py | 0.612657 | 0.335024 | game.py | pypi |
class MahjongRound:
def __init__(self, judger, dealer, num_players, np_random):
''' Initialize the round class
Args:
judger (object): the object of MahjongJudger
dealer (object): the object of MahjongDealer
num_players (int): the number of players in game
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/mahjong/round.py | 0.559892 | 0.312459 | round.py | pypi |
from termcolor import colored
class UnoCard:
info = {'type': ['number', 'action', 'wild'],
'color': ['r', 'g', 'b', 'y'],
'trait': ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'skip', 'reverse', 'draw_2', 'wild', 'wild_draw_4']
}
def __init__(s... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/uno/card.py | 0.59561 | 0.285795 | card.py | pypi |
from copy import deepcopy
import numpy as np
from rlcard.games.uno import Dealer
from rlcard.games.uno import Player
from rlcard.games.uno import Round
class UnoGame:
# def __init__(self, allow_step_back=False, num_players=2):
def __init__(self, allow_step_back=False):
self.allow_step_back = allow_st... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/uno/game.py | 0.753557 | 0.245797 | game.py | pypi |
import os
import json
import numpy as np
from collections import OrderedDict
import rlcard
from rlcard.games.uno.card import UnoCard as Card
# Read required docs
ROOT_PATH = rlcard.__path__[0]
# a map of abstract action to its index and a list of abstract action
with open(os.path.join(ROOT_PATH, 'games/uno/jsondata... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/uno/utils.py | 0.455441 | 0.236406 | utils.py | pypi |
from copy import deepcopy, copy
import numpy as np
from rlcard.games.limitholdem import Dealer
from rlcard.games.limitholdem import Player, PlayerStatus
from rlcard.games.limitholdem import Judger
from rlcard.games.limitholdem import Round
class LimitHoldemGame:
def __init__(self, allow_step_back=False, num_play... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/limitholdem/game.py | 0.770767 | 0.287561 | game.py | pypi |
class LimitHoldemRound:
"""Round can call other Classes' functions to keep the game running"""
def __init__(self, raise_amount, allowed_raise_num, num_players, np_random):
"""
Initialize the round class
Args:
raise_amount (int): the raise amount for each raise
a... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/limitholdem/round.py | 0.910496 | 0.633609 | round.py | pypi |
from enum import Enum
import numpy as np
from copy import deepcopy
from rlcard.games.limitholdem import Game
from rlcard.games.limitholdem import PlayerStatus
from rlcard.games.nolimitholdem import Dealer
from rlcard.games.nolimitholdem import Player
from rlcard.games.nolimitholdem import Judger
from rlcard.games.nol... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/nolimitholdem/game.py | 0.624866 | 0.205615 | game.py | pypi |
"""Implement no limit texas holdem Round class"""
from enum import Enum
from rlcard.games.limitholdem import PlayerStatus
class Action(Enum):
FOLD = 0
CHECK_CALL = 1
#CALL = 2
# RAISE_3BB = 3
RAISE_HALF_POT = 2
RAISE_POT = 3
# RAISE_2POT = 5
ALL_IN = 4
# SMALL_BLIND = 7
# BIG_... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/nolimitholdem/round.py | 0.832475 | 0.35782 | round.py | pypi |
''' Implement Doudizhu Game class
'''
import functools
from heapq import merge
import numpy as np
from rlcard.games.doudizhu.utils import cards2str, doudizhu_sort_card, CARD_RANK_STR
from rlcard.games.doudizhu import Player
from rlcard.games.doudizhu import Round
from rlcard.games.doudizhu import Judger
class Doudiz... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/doudizhu/game.py | 0.49048 | 0.171651 | game.py | pypi |
''' Implement Doudizhu Player class
'''
import functools
from rlcard.games.doudizhu.utils import get_gt_cards
from rlcard.games.doudizhu.utils import cards2str, doudizhu_sort_card
class DoudizhuPlayer:
''' Player can store cards in the player's hand and the role,
determine the actions can be made according t... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/doudizhu/player.py | 0.646237 | 0.224363 | player.py | pypi |
import numpy as np
from .player import GinRummyPlayer
from .round import GinRummyRound
from .judge import GinRummyJudge
from .utils.settings import Settings, DealerForRound
from .utils.action_event import *
class GinRummyGame:
''' Game class. This class will interact with outer environment.
'''
def __i... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/gin_rummy/game.py | 0.534855 | 0.18717 | game.py | pypi |
from typing import List
from rlcard.games.base import Card
from .utils import utils
from .utils import melding
class GinRummyPlayer:
def __init__(self, player_id: int, np_random):
''' Initialize a GinRummy player class
Args:
player_id (int): id for the player
'''
s... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/gin_rummy/player.py | 0.691289 | 0.17515 | player.py | pypi |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from .utils.move import GinRummyMove
from typing import List
from rlcard.games.gin_rummy.dealer import GinRummyDealer
from .utils.action_event import DrawCardAction, PickUpDiscardAction, DeclareDeadHandAction
from .utils.action_event import DiscardAction, KnockA... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/gin_rummy/round.py | 0.695648 | 0.17266 | round.py | pypi |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ..game import GinRummyGame
from typing import Callable
from .action_event import *
from ..player import GinRummyPlayer
from .move import ScoreNorthMove, ScoreSouthMove
from .gin_rummy_error import GinRummyProgramError
from rlcard.games.gin_rummy.utils impor... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/gin_rummy/utils/scorers.py | 0.761361 | 0.251958 | scorers.py | pypi |
from typing import List, Iterable
import numpy as np
from rlcard.games.base import Card
from .gin_rummy_error import GinRummyProgramError
valid_rank = ['A', '2', '3', '4', '5', '6', '7', '8', '9', 'T', 'J', 'Q', 'K']
valid_suit = ['S', 'H', 'D', 'C']
rank_to_deadwood_value = {"A": 1, "2": 2, "3": 3, "4": 4, "5": 5... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/gin_rummy/utils/utils.py | 0.799599 | 0.319267 | utils.py | pypi |
from typing import List
from rlcard.games.base import Card
from rlcard.games.gin_rummy.utils import utils
from rlcard.games.gin_rummy.utils.gin_rummy_error import GinRummyProgramError
# ===============================================================
# Terminology:
# run_meld - three or more cards of same s... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/gin_rummy/utils/melding.py | 0.417509 | 0.250798 | melding.py | pypi |
from typing import List
import numpy as np
from .judger import BridgeJudger
from .round import BridgeRound
from .utils.action_event import ActionEvent, CallActionEvent, PlayCardAction
class BridgeGame:
''' Game class. This class will interact with outer environment.
'''
def __init__(self, allow_step_ba... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/bridge/game.py | 0.780788 | 0.177383 | game.py | pypi |
from typing import List
from .dealer import BridgeDealer
from .player import BridgePlayer
from .utils.action_event import CallActionEvent, PassAction, DblAction, RdblAction, BidAction, PlayCardAction
from .utils.move import BridgeMove, DealHandMove, PlayCardMove, MakeBidMove, MakePassMove, MakeDblMove, MakeRdblMove, ... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/bridge/round.py | 0.787523 | 0.178204 | round.py | pypi |
from typing import List
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from .game import BridgeGame
from .utils.action_event import PlayCardAction
from .utils.action_event import ActionEvent, BidAction, PassAction, DblAction, RdblAction
from .utils.move import MakeBidMove, MakeDblMove, MakeRdblMove
from .util... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/bridge/judger.py | 0.663778 | 0.160825 | judger.py | pypi |
class BlackjackJudger:
def __init__(self, np_random):
''' Initialize a BlackJack judger class
'''
self.np_random = np_random
self.rank2score = {"A":11, "2":2, "3":3, "4":4, "5":5, "6":6, "7":7, "8":8, "9":9, "T":10, "J":10, "Q":10, "K":10}
def judge_round(self, player):
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/blackjack/judger.py | 0.727685 | 0.46308 | judger.py | pypi |
import numpy as np
from copy import copy
from rlcard.games.leducholdem import Dealer
from rlcard.games.leducholdem import Player
from rlcard.games.leducholdem import Judger
from rlcard.games.leducholdem import Round
from rlcard.games.limitholdem import Game
class LeducholdemGame(Game):
def __init__(self, allow_... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/leducholdem/game.py | 0.71103 | 0.314471 | game.py | pypi |
from rlcard.utils.utils import rank2int
class LeducholdemJudger:
''' The Judger class for Leduc Hold'em
'''
def __init__(self, np_random):
''' Initialize a judger class
'''
self.np_random = np_random
@staticmethod
def judge_game(players, public_card):
''' Judge the ... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/games/leducholdem/judger.py | 0.673943 | 0.356503 | judger.py | pypi |
import numpy as np
import collections
import os
import pickle
from rlcard.utils.utils import *
class CFRAgent():
''' Implement CFR (chance sampling) algorithm
'''
def __init__(self, env, model_path='./cfr_model'):
''' Initilize Agent
Args:
env (Env): Env class
'''
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/cfr_agent.py | 0.74382 | 0.323086 | cfr_agent.py | pypi |
import random
import numpy as np
import torch
import torch.nn as nn
from collections import namedtuple
from copy import deepcopy
from rlcard.utils.utils import remove_illegal
Transition = namedtuple('Transition', ['state', 'action', 'reward', 'next_state', 'legal_actions', 'done'])
class DQNAgent(object):
'''
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/dqn_agent.py | 0.88981 | 0.519521 | dqn_agent.py | pypi |
import random
import collections
import enum
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from rlcard.agents.dqn_agent import DQNAgent
from rlcard.utils.utils import remove_illegal
Transition = collections.namedtuple('Transition', 'info_state action_probs')
class NFSPAgent(ob... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/nfsp_agent.py | 0.850189 | 0.429669 | nfsp_agent.py | pypi |
from rlcard.utils.utils import print_card
class HumanAgent(object):
''' A human agent for Blackjack. It can be used to play alone for understand how the blackjack code runs
'''
def __init__(self, num_actions):
''' Initilize the human agent
Args:
num_actions (int): the size of... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/human_agents/blackjack_human_agent.py | 0.68616 | 0.522019 | blackjack_human_agent.py | pypi |
from rlcard.games.uno.card import UnoCard
class HumanAgent(object):
''' A human agent for Leduc Holdem. It can be used to play against trained models
'''
def __init__(self, num_actions):
''' Initilize the human agent
Args:
num_actions (int): the size of the ouput action space
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/human_agents/uno_human_agent.py | 0.676834 | 0.391144 | uno_human_agent.py | pypi |
from rlcard.utils.utils import print_card
class HumanAgent(object):
''' A human agent for Limit Holdem. It can be used to play against trained models
'''
def __init__(self, num_actions):
''' Initilize the human agent
Args:
num_actions (int): the size of the ouput action space... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/human_agents/limit_holdem_human_agent.py | 0.675551 | 0.501221 | limit_holdem_human_agent.py | pypi |
from rlcard.utils.utils import print_card
class HumanAgent(object):
''' A human agent for No Limit Holdem. It can be used to play against trained models
'''
def __init__(self, num_actions):
''' Initilize the human agent
Args:
num_actions (int): the size of the ouput action sp... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/human_agents/nolimit_holdem_human_agent.py | 0.705684 | 0.431824 | nolimit_holdem_human_agent.py | pypi |
from rlcard.utils.utils import print_card
class HumanAgent(object):
''' A human agent for Leduc Holdem. It can be used to play against trained models
'''
def __init__(self, num_actions):
''' Initilize the human agent
Args:
num_actions (int): the size of the ouput action space... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/human_agents/leduc_holdem_human_agent.py | 0.603815 | 0.475423 | leduc_holdem_human_agent.py | pypi |
import time
from rlcard.games.gin_rummy.utils.action_event import ActionEvent
from rlcard.games.gin_rummy.utils.gin_rummy_error import GinRummyProgramError
class HumanAgent(object):
''' A human agent for Gin Rummy. It can be used to play against trained models.
'''
def __init__(self, num_actions):
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/human_agents/gin_rummy_human_agent/gin_rummy_human_agent.py | 0.670716 | 0.264067 | gin_rummy_human_agent.py | pypi |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from .game_canvas import GameCanvas
from typing import List
from rlcard.games.gin_rummy.game import GinRummyGame
from rlcard.games.gin_rummy.utils.action_event import DrawCardAction, PickUpDiscardAction, DeclareDeadHandAction
from rlcard.games.gin_rummy.utils.ac... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/human_agents/gin_rummy_human_agent/gui_gin_rummy/game_canvas_query.py | 0.732209 | 0.166337 | game_canvas_query.py | pypi |
import os
import threading
import time
import timeit
import pprint
from collections import deque
import torch
from torch import multiprocessing as mp
from torch import nn
from .file_writer import FileWriter
from .model import DMCModel
from .pettingzoo_model import DMCModelPettingZoo
from .utils import (
get_batc... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/dmc_agent/trainer.py | 0.818338 | 0.303758 | trainer.py | pypi |
import numpy as np
import torch
from torch import nn
class DMCNet(nn.Module):
def __init__(
self,
state_shape,
action_shape,
mlp_layers=[512,512,512,512,512]
):
super().__init__()
input_dim = np.prod(state_shape) + np.prod(action_shape)
layer_dims = [in... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/agents/dmc_agent/model.py | 0.879903 | 0.583381 | model.py | pypi |
import numpy as np
from collections import OrderedDict
from rlcard.envs import Env
from rlcard.games.mahjong import Game
from rlcard.games.mahjong import Card
from rlcard.games.mahjong.utils import card_encoding_dict, encode_cards, pile2list
class MahjongEnv(Env):
''' Mahjong Environment
'''
def __init__... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/envs/mahjong.py | 0.469763 | 0.230205 | mahjong.py | pypi |
import importlib
# Default Config
DEFAULT_CONFIG = {
'allow_step_back': False,
'seed': None,
}
class EnvSpec(object):
''' A specification for a particular instance of the environment.
'''
def __init__(self, env_id, entry_point=None):
''' Initilize
Args:
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/envs/registration.py | 0.668664 | 0.157266 | registration.py | pypi |
import numpy as np
from collections import OrderedDict
from rlcard.envs import Env
from rlcard.games.blackjack import Game
DEFAULT_GAME_CONFIG = {
'game_num_players': 1,
'game_num_decks': 1
}
class BlackjackEnv(Env):
''' Blackjack Environment
'''
def __init__(self, config):
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/envs/blackjack.py | 0.58818 | 0.264495 | blackjack.py | pypi |
import json
import os
import numpy as np
from collections import OrderedDict
import rlcard
from rlcard.envs import Env
from rlcard.games.limitholdem import Game
DEFAULT_GAME_CONFIG = {
'game_num_players': 2,
}
class LimitholdemEnv(Env):
''' Limitholdem Environment
'''
def __init__(self, ... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/envs/limitholdem.py | 0.584983 | 0.22762 | limitholdem.py | pypi |
from collections import Counter, OrderedDict
import numpy as np
from rlcard.envs import Env
class DoudizhuEnv(Env):
''' Doudizhu Environment
'''
def __init__(self, config):
from rlcard.games.doudizhu.utils import ACTION_2_ID, ID_2_ACTION
from rlcard.games.doudizhu.utils import cards2str,... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/envs/doudizhu.py | 0.598547 | 0.208723 | doudizhu.py | pypi |
import json
import os
import numpy as np
from collections import OrderedDict
import rlcard
from rlcard.envs import Env
from rlcard.games.leducholdem import Game
from rlcard.utils import *
DEFAULT_GAME_CONFIG = {
'game_num_players': 2,
}
class LeducholdemEnv(Env):
''' Leduc Hold'em Environment
... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/envs/leducholdem.py | 0.589362 | 0.210584 | leducholdem.py | pypi |
from rlcard.utils import *
class Env(object):
'''
The base Env class. For all the environments in RLCard,
we should base on this class and implement as many functions
as we can.
'''
def __init__(self, config):
''' Initialize the environment
Args:
config (dict): A co... | /rlcard-uno-2.0.2.tar.gz/rlcard-uno-2.0.2/rlcard/envs/env.py | 0.650578 | 0.336726 | env.py | pypi |
# RLCard: A Toolkit for Reinforcement Learning in Card Games
<img width="500" src="https://dczha.com/files/rlcard/logo.jpg" alt="Logo" />
[](https://github.com/datamllab/rlcard/actions/workflows/python-package.yml)
[![PyPI ve... | /rlcard-1.2.0.tar.gz/rlcard-1.2.0/README.md | 0.530966 | 0.918261 | README.md | pypi |
import requests
class RLClient:
def __init__(self, name, password, hostname="10.216.3.238"):
self.TEAM_NAME = name
self.TEAM_PASSWORD = password
self.SERVER = "http://" + hostname + ":80/rl"
@staticmethod
def validate_ids(run_id, request_number):
if run_id < 0:
... | /rlclientbdr-1.11.tar.gz/rlclientbdr-1.11/rlclient/client.py | 0.47926 | 0.155335 | client.py | pypi |
import numpy as np
import networkx as nx
def _rho(D, target_fraction=0.02, mode='gaussian'):
"""Calculates the RL rho values from a distance matrix"""
dcut = np.sort(D)[:,1 + int(len(D) * target_fraction)].mean()
if mode == 'classic':
r = np.array([len(np.where(d < dcut)[0]) for d in D])
elif ... | /rlcluster-0.0.5.tar.gz/rlcluster-0.0.5/rlcluster.py | 0.528533 | 0.635873 | rlcluster.py | pypi |
`rlda`: Robust Latent Dirichlet Allocation models
-------------------------
This python module provides a set of functions to fit multiple LDA models to a
text corpus and then search for the robust topics present in multiple models.
In natural language processing LDA models are used to classify text into topics. Ho... | /rlda-0.61.tar.gz/rlda-0.61/README.rst | 0.910051 | 0.759894 | README.rst | pypi |
# coding=utf-8
"""TFDS episode writer."""
from typing import Optional
from absl import logging
from rlds import rlds_types
import tensorflow_datasets as tfds
DatasetConfig = tfds.rlds.rlds_base.DatasetConfig
class EpisodeWriter():
"""Class that writes trajectory data in TFDS format (and RLDS structure)."""
d... | /tfds/episode_writer.py | 0.874158 | 0.328314 | episode_writer.py | pypi |
# coding=utf-8
"""Library to generate a TFDS config."""
from typing import Any, Dict, List, Optional, Union
import numpy as np
from rlds import rlds_types
import tensorflow as tf
import tensorflow_datasets as tfds
_STEP_KEYS = [
rlds_types.OBSERVATION, rlds_types.ACTION, rlds_types.DISCOUNT,
rlds_types.REWA... | /tfds/config_generator.py | 0.927223 | 0.495178 | config_generator.py | pypi |
import time
import scipy.optimize
import theano
from rllab.core import Serializable
from rllab.misc import compile_function
from rllab.misc import flatten_tensor_variables
from rllab.misc import lazydict
class LbfgsOptimizer(Serializable):
"""
Performs unconstrained optimization via L-BFGS.
"""
def... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/optimizers/lbfgs_optimizer.py | 0.847179 | 0.249802 | lbfgs_optimizer.py | pypi |
from _ast import Num
import itertools # noqa: I100,I201
import numpy as np
import theano
import theano.tensor as TT
from rllab.core import Serializable
from rllab.misc import ext
from rllab.misc import krylov
from rllab.misc import logger
from rllab.misc import sliced_fun
class PerlmutterHvp(Serializable):
def... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/optimizers/conjugate_gradient_optimizer.py | 0.765155 | 0.23557 | conjugate_gradient_optimizer.py | pypi |
import time
from rllab.core import Serializable
from rllab.misc import compile_function,
from rllab.misc import lazydict
from rllab.optimizers import BatchDataset
from rllab.optimizers import hf_optimizer
class HessianFreeOptimizer(Serializable):
"""
Performs unconstrained optimization via Hessian-Free Optim... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/optimizers/hessian_free_optimizer.py | 0.858006 | 0.205535 | hessian_free_optimizer.py | pypi |
from collections import OrderedDict
from functools import partial
import time
import lasagne.updates
import pyprind
import theano
from rllab.core import Serializable
from rllab.misc import ext
from rllab.misc import logger
from rllab.optimizers import BatchDataset
class FirstOrderOptimizer(Serializable):
"""
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/optimizers/first_order_optimizer.py | 0.902014 | 0.22718 | first_order_optimizer.py | pypi |
import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano
import theano.tensor as TT
from rllab.core import ConvNetwork
from rllab.core import LasagnePowered
from rllab.core import ParamLayer
from rllab.core import Serializable
from rllab.distributions import Diagon... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/regressors/gaussian_conv_regressor.py | 0.841468 | 0.325601 | gaussian_conv_regressor.py | pypi |
import numpy as np
from rllab.core import Serializable
class ProductRegressor(Serializable):
"""
A class for performing MLE regression by fitting a product distribution to
the outputs. A separate regressor will be trained for each individual input
distribution.
"""
def __init__(self, regress... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/regressors/product_regressor.py | 0.805403 | 0.524699 | product_regressor.py | pypi |
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano
import theano.tensor as TT
from rllab.core import LasagnePowered
from rllab.core import MLP
from rllab.core import Serializable
from rllab.distributions import Categorical
from rllab.misc import ext
from rllab.misc import l... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/regressors/categorical_mlp_regressor.py | 0.825941 | 0.357988 | categorical_mlp_regressor.py | pypi |
import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano
import theano.tensor as TT
from rllab.core import LasagnePowered
from rllab.core import MLP
from rllab.core import ParamLayer
from rllab.core import Serializable
from rllab.distributions import DiagonalGaussi... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/regressors/gaussian_mlp_regressor.py | 0.788217 | 0.394376 | gaussian_mlp_regressor.py | pypi |
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
from rllab.core import ConvNetwork
from rllab.core import LasagnePowered
from rllab.core import Serializable
from rllab.distributions import Categorical
from rllab.misc import ext
from rllab.misc import logger
from rllab.misc import tens... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/policies/categorical_conv_policy.py | 0.920057 | 0.437463 | categorical_conv_policy.py | pypi |
import lasagne.init
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano.tensor as TT
from rllab.core import GRUNetwork
from rllab.core import LasagnePowered
from rllab.core import ParamLayer
from rllab.core import Serializable
from rllab.distributions import RecurrentDiagona... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/policies/gaussian_gru_policy.py | 0.790247 | 0.404802 | gaussian_gru_policy.py | pypi |
from rllab.core import Parameterized
class Policy(Parameterized):
def __init__(self, env_spec):
Parameterized.__init__(self)
self._env_spec = env_spec
# Should be implemented by all policies
def get_action(self, observation):
raise NotImplementedError
def reset(self):
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/policies/base.py | 0.889018 | 0.398055 | base.py | pypi |
import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano.tensor as TT
from rllab.core import LasagnePowered
from rllab.core import MLP
from rllab.core import ParamLayer
from rllab.core import Serializable
from rllab.distributions import DiagonalGaussian
from rllab.... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/policies/gaussian_mlp_policy.py | 0.85984 | 0.363336 | gaussian_mlp_policy.py | pypi |
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
from rllab.core import LasagnePowered
from rllab.core import MLP
from rllab.core import Serializable
from rllab.distributions import Categorical
from rllab.misc import ext
from rllab.misc.overrides import overrides
from rllab.policies im... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/policies/categorical_mlp_policy.py | 0.867457 | 0.433322 | categorical_mlp_policy.py | pypi |
import lasagne
import lasagne.init as LI
import lasagne.layers as L
import lasagne.nonlinearities as NL
from rllab.core import batch_norm
from rllab.core import LasagnePowered
from rllab.core import Serializable
from rllab.misc import ext
from rllab.policies import Policy
class DeterministicMLPPolicy(Policy, Lasagne... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/policies/deterministic_mlp_policy.py | 0.768907 | 0.237642 | deterministic_mlp_policy.py | pypi |
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano.tensor as TT
from rllab.core import GRUNetwork
from rllab.core import LasagnePowered
from rllab.core import OpLayer
from rllab.core import Serializable
from rllab.distributions import RecurrentCategorical
from rllab.misc im... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/policies/categorical_gru_policy.py | 0.759939 | 0.347565 | categorical_gru_policy.py | pypi |
from rllab.algos import RLAlgorithm
import rllab.misc.logger as logger
from rllab.plotter import plotter
from rllab.policies import Policy
from rllab.sampler import parallel_sampler
from rllab.sampler.base import BaseSampler
from rllab.sampler.utils import rollout
class BatchSampler(BaseSampler):
def __init__(sel... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/algos/batch_polopt.py | 0.817064 | 0.309363 | batch_polopt.py | pypi |
import theano
import theano.tensor as TT
from rllab.algos import BatchPolopt
from rllab.core import Serializable
from rllab.misc import ext
from rllab.misc import logger
from rllab.misc.overrides import overrides
from rllab.optimizers import FirstOrderOptimizer
class VPG(BatchPolopt, Serializable):
"""
Vanil... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/algos/vpg.py | 0.661267 | 0.151906 | vpg.py | pypi |
import numpy as np
import theano.tensor as TT
from rllab.algos import cma_es_lib
from rllab.algos import RLAlgorithm
from rllab.core import Serializable
from rllab.misc import ext
import rllab.misc.logger as logger
from rllab.misc.special import discount_cumsum
import rllab.plotter as plotter
from rllab.sampler import... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/algos/cma_es.py | 0.727395 | 0.401629 | cma_es.py | pypi |
import theano
import theano.tensor as TT
from rllab.algos import BatchPolopt
from rllab.misc import ext
import rllab.misc.logger as logger
from rllab.misc.overrides import overrides
from rllab.optimizers import PenaltyLbfgsOptimizer
class NPO(BatchPolopt):
"""
Natural Policy Optimization.
"""
def __... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/algos/npo.py | 0.649579 | 0.185412 | npo.py | pypi |
import numpy as np
import theano.tensor as TT
from rllab.distributions import Distribution
TINY = 1e-8
class Bernoulli(Distribution):
def __init__(self, dim):
self._dim = dim
@property
def dim(self):
return self._dim
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/distributions/bernoulli.py | 0.729038 | 0.161849 | bernoulli.py | pypi |
import numpy as np
import theano
import theano.tensor as TT
from rllab.distributions import Categorical
from rllab.distributions import Distribution
TINY = 1e-8
class RecurrentCategorical(Distribution):
def __init__(self, dim):
self._cat = Categorical(dim)
self._dim = dim
@property
def ... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/distributions/recurrent_categorical.py | 0.841858 | 0.27312 | recurrent_categorical.py | pypi |
import numpy as np
import theano.tensor as TT
from rllab.distributions import Distribution
class DiagonalGaussian(Distribution):
def __init__(self, dim):
self._dim = dim
@property
def dim(self):
return self._dim
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
old_m... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/distributions/diagonal_gaussian.py | 0.822759 | 0.306864 | diagonal_gaussian.py | pypi |
import lasagne
import lasagne.layers as L
import theano
import theano.tensor as TT
class ParamLayer(L.Layer):
def __init__(self,
incoming,
num_units,
param=lasagne.init.Constant(0.),
trainable=True,
**kwargs):
super(Param... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/core/lasagne_layers.py | 0.885792 | 0.525551 | lasagne_layers.py | pypi |
from contextlib import contextmanager
from rllab.core import Serializable
from rllab.misc.tensor_utils import flatten_tensors, unflatten_tensors
load_params = True
@contextmanager
def suppress_params_loading():
global load_params
load_params = False
yield
load_params = True
class Parameterized(Ser... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/core/parameterized.py | 0.813683 | 0.250517 | parameterized.py | pypi |
import numpy as np
from rllab.misc import special
from rllab.misc import tensor_utils
import rllab.misc.logger as logger
from rllab.sampler import utils
class Sampler(object):
def start_worker(self):
"""
Initialize the sampler, e.g. launching parallel workers if necessary.
"""
rai... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/sampler/base.py | 0.756447 | 0.301137 | base.py | pypi |
import lasagne
import lasagne.init
import lasagne.layers as L
import lasagne.nonlinearities as NL
import theano.tensor as TT
from rllab.core import batch_norm
from rllab.core import LasagnePowered
from rllab.core import Serializable
from rllab.misc import ext
from rllab.q_functions import QFunction
class ContinuousM... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/q_functions/continuous_mlp_q_function.py | 0.67971 | 0.270336 | continuous_mlp_q_function.py | pypi |
import numpy as np
from rllab.misc import ext
from rllab.spaces import Space
class Product(Space):
def __init__(self, *components):
if isinstance(components[0], (list, tuple)):
assert len(components) == 1
components = components[0]
self._components = tuple(components)
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/spaces/product.py | 0.62395 | 0.421611 | product.py | pypi |
import numpy as np
import theano
from rllab.core import Serializable
from rllab.misc import ext
from rllab.spaces import Space
class Box(Space):
"""
A box in R^n.
I.e., each coordinate is bounded.
"""
def __init__(self, low, high, shape=None):
"""
Two kinds of valid input:
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/spaces/box.py | 0.842053 | 0.585457 | box.py | pypi |
from cached_property import cached_property
import numpy as np
from rllab import spaces
from rllab.core import Serializable
from rllab.envs import Step
from rllab.envs.mujoco import MujocoEnv
from rllab.envs.proxy_env import ProxyEnv
from rllab.misc.overrides import overrides
BIG = 1e6
class OcclusionEnv(ProxyEnv, ... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/occlusion_env.py | 0.855187 | 0.370168 | occlusion_env.py | pypi |
import collections
from cached_property import cached_property
from rllab.envs import EnvSpec
class Env(object):
def step(self, action):
"""
Run one timestep of the environment's dynamics. When end of episode
is reached, reset() should be called to reset the environment's internal
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/base.py | 0.909739 | 0.535524 | base.py | pypi |
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