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Convert code to python 2.7 (#35)

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* Convert code format to python2.7 (SAC)

* Convert code format python2.7 (TD3, all fD)

* Remove no use import and black setting

* Change SAC param

* Change env name Reacher-v2 to v1

* Remove old version reacher training script

* Convert code format python2.7

* Modify .travis.yml

* Add install command python3.6 & black on Makefile

* Fix seperator to tab on Makefile

* Modify Makefile

* Fix little error

* Change td3 gamma parameter
This commit is contained in:
Kyunghwan Kim
2019-03-25 19:07:19 +09:00
committed by Whi Kwon
parent 81a9d861b6
commit d2769dfa9d
39 changed files with 154 additions and 286 deletions
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scripts/algorithms/common/helper_functions.py
+6 -13
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@@ -7,28 +7,25 @@
import random
from collections import deque
from typing import Deque, List, Tuple
import gym
import numpy as np
import torch
import torch.nn as nn
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def identity(x: torch.Tensor) -> torch.Tensor:
def identity(x):
"""Return input without any change."""
return x
def soft_update(local: nn.Module, target: nn.Module, tau: float):
def soft_update(local, target, tau):
"""Soft-update: target = tau*local + (1-tau)*target."""
for t_param, l_param in zip(target.parameters(), local.parameters()):
t_param.data.copy_(tau * l_param.data + (1.0 - tau) * t_param.data)
def set_random_seed(seed: int, env: gym.Env):
def set_random_seed(seed, env):
"""Set random seed"""
env.seed(seed)
torch.manual_seed(seed)
@@ -36,16 +33,14 @@ def set_random_seed(seed: int, env: gym.Env):
random.seed(seed)
def get_n_step_info_from_demo(
demo: List, n_step: int, gamma: float
) -> Tuple[List, List]:
def get_n_step_info_from_demo(demo, n_step, gamma):
"""Return 1 step and n step demos."""
assert demo
assert n_step > 1
demos_1_step = list()
demos_n_step = list()
n_step_buffer: Deque = deque(maxlen=n_step)
n_step_buffer = deque(maxlen=n_step)
for transition in demo:
n_step_buffer.append(transition)
@@ -63,9 +58,7 @@ def get_n_step_info_from_demo(
return demos_1_step, demos_n_step
def get_n_step_info(
n_step_buffer: Deque, gamma: float
) -> Tuple[np.int64, np.ndarray, bool]:
def get_n_step_info(n_step_buffer, gamma):
"""Return n step reward, next state, and done."""
# info of the last transition
reward, next_state, done = n_step_buffer[-1][-3:]