File size: 6,661 Bytes
8bf4dee
 
 
 
0e936e1
 
 
 
 
 
 
8bf4dee
 
 
 
 
 
 
 
 
 
0e936e1
 
 
 
 
 
8bf4dee
0e936e1
8bf4dee
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e936e1
8bf4dee
0e936e1
 
8bf4dee
0e936e1
 
8bf4dee
 
 
 
0e936e1
 
8bf4dee
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e936e1
 
 
 
 
 
 
 
 
 
8bf4dee
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e936e1
 
8bf4dee
 
 
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
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
import argparse
import json
import os
import random
from dataclasses import asdict
from pathlib import Path
from typing import Dict, Optional, Type, Union

import gym
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.backends.cudnn
import yaml
from gym.spaces import Box, Discrete
from torch.utils.tensorboard.writer import SummaryWriter

from rl_algo_impls.a2c.a2c import A2C
from rl_algo_impls.dqn.dqn import DQN
from rl_algo_impls.dqn.policy import DQNPolicy
from rl_algo_impls.ppo.ppo import PPO
from rl_algo_impls.runner.config import Config, Hyperparams
from rl_algo_impls.shared.algorithm import Algorithm
from rl_algo_impls.shared.callbacks.eval_callback import EvalCallback
from rl_algo_impls.shared.policy.on_policy import ActorCritic
from rl_algo_impls.shared.policy.policy import Policy
from rl_algo_impls.shared.vec_env.utils import import_for_env_id, is_microrts
from rl_algo_impls.vpg.policy import VPGActorCritic
from rl_algo_impls.vpg.vpg import VanillaPolicyGradient
from rl_algo_impls.wrappers.vectorable_wrapper import VecEnv, single_observation_space

ALGOS: Dict[str, Type[Algorithm]] = {
    "dqn": DQN,
    "vpg": VanillaPolicyGradient,
    "ppo": PPO,
    "a2c": A2C,
}
POLICIES: Dict[str, Type[Policy]] = {
    "dqn": DQNPolicy,
    "vpg": VPGActorCritic,
    "ppo": ActorCritic,
    "a2c": ActorCritic,
}

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",
    )
    return parser


def load_hyperparams(algo: str, env_id: str) -> Hyperparams:
    root_path = Path(__file__).parent.parent
    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 env_id in hyperparams_dict:
        return Hyperparams(**hyperparams_dict[env_id])

    import_for_env_id(env_id)
    spec = gym.spec(env_id)
    entry_point_name = str(spec.entry_point)  # type: ignore
    if "AtariEnv" in entry_point_name and "_atari" in hyperparams_dict:
        return Hyperparams(**hyperparams_dict["_atari"])
    elif "gym_microrts" in entry_point_name and "_microrts" in hyperparams_dict:
        return Hyperparams(**hyperparams_dict["_microrts"])
    else:
        raise ValueError(f"{env_id} not specified in {algo} hyperparameters file")


def get_device(config: Config, env: VecEnv) -> torch.device:
    device = config.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 = single_observation_space(env)
        if isinstance(obs_space, Discrete):
            device = "cpu"
        elif isinstance(obs_space, Box) and len(obs_space.shape) == 1:
            device = "cpu"
        if is_microrts(config):
            try:
                from gym_microrts.envs.vec_env import MicroRTSGridModeVecEnv

                # Models that move more than one unit at a time should use mps
                if not isinstance(env.unwrapped, MicroRTSGridModeVecEnv):
                    device = "cpu"
            except ModuleNotFoundError:
                # Likely on gym_microrts v0.0.2 to match ppo-implementation-details
                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"
    # Stop warning and it would introduce stochasticity if I was using TF
    os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"


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 hparam_dict(
    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 asdict(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
        elif isinstance(v, list):
            flattened[k] = json.dumps(v)
        else:
            flattened[k] = v  # type: ignore
    return flattened  # type: ignore