File size: 5,527 Bytes
341188c df508c1 341188c 69aa193 341188c df508c1 341188c 69aa193 341188c df508c1 341188c 69aa193 341188c df508c1 341188c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
import argparse
import gym
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import torch
import torch.backends.cudnn
import yaml
from gym.spaces import Box, Discrete
from stable_baselines3.common.vec_env.base_vec_env import VecEnv
from torch.utils.tensorboard.writer import SummaryWriter
from typing import Dict, Optional, Type, Union
from runner.config import Hyperparams
from shared.algorithm import Algorithm
from shared.callbacks.eval_callback import EvalCallback
from shared.policy.policy import Policy
from dqn.dqn import DQN
from dqn.policy import DQNPolicy
from vpg.vpg import VanillaPolicyGradient
from vpg.policy import VPGActorCritic
from ppo.ppo import PPO
from ppo.policy import PPOActorCritic
ALGOS: Dict[str, Type[Algorithm]] = {
"dqn": DQN,
"vpg": VanillaPolicyGradient,
"ppo": PPO,
}
POLICIES: Dict[str, Type[Policy]] = {
"dqn": DQNPolicy,
"vpg": VPGActorCritic,
"ppo": PPOActorCritic,
}
HYPERPARAMS_PATH = "hyperparams"
def base_parser(multiple: bool = True) -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument(
"--algo",
default=["dqn"],
type=str,
choices=list(ALGOS.keys()),
nargs="+" if multiple else 1,
help="Abbreviation(s) of algorithm(s)",
)
parser.add_argument(
"--env",
default=["CartPole-v1"],
type=str,
nargs="+" if multiple else 1,
help="Name of environment(s) in gym",
)
parser.add_argument(
"--seed",
default=[1],
type=int,
nargs="*" if multiple else "?",
help="Seeds to run experiment. Unset will do one run with no set seed",
)
parser.add_argument(
"--use-deterministic-algorithms",
default=True,
type=bool,
help="If seed set, set torch.use_deterministic_algorithms",
)
return parser
def load_hyperparams(algo: str, env_id: str, root_path: str) -> Hyperparams:
hyperparams_path = os.path.join(root_path, HYPERPARAMS_PATH, f"{algo}.yml")
with open(hyperparams_path, "r") as f:
hyperparams_dict = yaml.safe_load(f)
if "BulletEnv" in env_id:
import pybullet_envs
spec = gym.spec(env_id)
if env_id in hyperparams_dict:
return hyperparams_dict[env_id]
elif "AtariEnv" in str(spec.entry_point) and "atari" in hyperparams_dict:
return hyperparams_dict["atari"]
else:
raise ValueError(f"{env_id} not specified in {algo} hyperparameters file")
def get_device(device: str, env: VecEnv) -> torch.device:
# cuda by default
if device == "auto":
device = "cuda"
# Apple MPS is a second choice (sometimes)
if device == "cuda" and not torch.cuda.is_available():
device = "mps"
# If no MPS, fallback to cpu
if device == "mps" and not torch.backends.mps.is_available():
device = "cpu"
# Simple environments like Discreet and 1-D Boxes might also be better
# served with the CPU.
if device == "mps":
obs_space = env.observation_space
if isinstance(obs_space, Discrete):
device = "cpu"
elif isinstance(obs_space, Box) and len(obs_space.shape) == 1:
device = "cpu"
print(f"Device: {device}")
return torch.device(device)
def set_seeds(seed: Optional[int], use_deterministic_algorithms: bool) -> None:
if seed is None:
return
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.use_deterministic_algorithms(use_deterministic_algorithms)
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
def make_policy(
algo: str,
env: VecEnv,
device: torch.device,
load_path: Optional[str] = None,
**kwargs,
) -> Policy:
policy = POLICIES[algo](env, **kwargs).to(device)
if load_path:
policy.load(load_path)
return policy
def plot_eval_callback(callback: EvalCallback, tb_writer: SummaryWriter, run_name: str):
figure = plt.figure()
cumulative_steps = [
(idx + 1) * callback.step_freq for idx in range(len(callback.stats))
]
plt.plot(
cumulative_steps,
[s.score.mean for s in callback.stats],
"b-",
label="mean",
)
plt.plot(
cumulative_steps,
[s.score.mean - s.score.std for s in callback.stats],
"g--",
label="mean-std",
)
plt.fill_between(
cumulative_steps,
[s.score.min for s in callback.stats], # type: ignore
[s.score.max for s in callback.stats], # type: ignore
facecolor="cyan",
label="range",
)
plt.xlabel("Steps")
plt.ylabel("Score")
plt.legend()
plt.title(f"Eval {run_name}")
tb_writer.add_figure("eval", figure)
Scalar = Union[bool, str, float, int, None]
def flatten_hyperparameters(
hyperparams: Hyperparams, args: Dict[str, Union[Scalar, list]]
) -> Dict[str, Scalar]:
flattened = args.copy()
for k, v in flattened.items():
if isinstance(v, list):
flattened[k] = json.dumps(v)
for k, v in hyperparams.items():
if isinstance(v, dict):
for sk, sv in v.items():
key = f"{k}/{sk}"
if isinstance(sv, dict) or isinstance(sv, list):
flattened[key] = str(sv)
else:
flattened[key] = sv
else:
flattened[k] = v # type: ignore
return flattened # type: ignore
|