dacbench.envs.fast_downward

Planning environment from “Learning Heuristic Selection with Dynamic Algorithm Configuration” by David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller und Marius Lindauer. Original environment authors: David Speck, André Biedenkapp

Module Contents

Classes

StateType

Class to define numbers for state types

FastDownwardEnv

Environment to control Solver Heuristics of FastDownward
class dacbench.envs.fast_downward.StateType

Bases: enum.Enum

Class to define numbers for state types

RAW = 1
DIFF = 2
ABSDIFF = 3
NORMAL = 4
NORMDIFF = 5
NORMABSDIFF = 6
class dacbench.envs.fast_downward.FastDownwardEnv(config)

Bases: dacbench.AbstractEnv

Environment to control Solver Heuristics of FastDownward

property port(self)
property argstring(self)
static _save_div(a, b)

Helper method for safe division

Parameters

  • a (list or np.array) – values to be divided

  • b (list or np.array) – values to divide by

Returns

Division result

Return type

np.array

send_msg(self, msg: bytes)

Send message and prepend the message size

Based on comment from SO see [1] [1] https://stackoverflow.com/a/17668009

Parameters

msg (bytes) – The message as byte

recv_msg(self)

Recieve a whole message. The message has to be prepended with its total size Based on comment from SO see [1]

Returns

The message as byte

Return type

bytes

recvall(self, n: int)

Given we know the size we want to recieve, we can recieve that amount of bytes. Based on comment from SO see [1]

Parameters

n (int) – Number of bytes to expect in the data

Returns

The message as byte

Return type

bytes

_process_data(self)

Split received json into state reward and done

Returns

state, reward, done

Return type

np.array, float, bool

step(self, action: Union[int, List[int]])

Environment step

Parameters

action (Union[int, List[int]]) – Parameter(s) to apply

Returns

state, reward, done, info

Return type

np.array, float, bool, dict

reset(self)

Reset environment

Returns

State after reset

Return type

np.array

kill_connection(self)

Kill the connection

close(self)

Close Env

Returns

Closing confirmation

Return type

bool

render(self, mode: str = 'human') → None

Required by gym.Env but not implemented

Parameters

mode (str) – Rendering mode