def main():
env = gym.make("LunarLander-v2")
agent = Agent(env)
agent.load_state_dict(torch.load("./models/agent.pt"))
agent.eval()
obs = env.reset()
done = False
for i in range(10000):
env.render()
obs = torch.from_numpy(obs).float()
action, _, _ = agent.get_action(obs)
obs, rew, done, info = env.step(action.cpu().numpy())
sleep(0.001)
if done:
obs = env.reset()
from gym import spaces
import cv2
cv2.ocl.setUseOpenCL(False)
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import gym
from model import Agent
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
env = gym.make("LunarLander-v2")
expert = Agent(env)
expert.load_state_dict(torch.load("./models/agent.pt"))
expert.eval()
def generate_rollout(agent, env):
agent.to("cpu")
states = []
actions = []
rewards = []
obs = env.reset()
steps = 0
while True:
obs = torch.from_numpy(obs).float()
states.append(obs)
logits = agent.forward(obs)
probs = torch.softmax(logits, dim=0)
action = probs.argmax()
# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/84a7582477fb0d5c82ad6d850fe476829dddd2e1/a2c_ppo_acktr/storage.py#L60
next_obs = envs.reset()
next_done = torch.zeros(args.num_envs).to(device)
num_updates = args.total_timesteps // args.batch_size
## CRASH AND RESUME LOGIC:
starting_update = 1
if args.prod_mode and wandb.run.resumed:
print("previous run.summary", run.summary)
starting_update = run.summary['charts/update'] + 1
global_step = starting_update * args.batch_size
api = wandb.Api()
run = api.run(run.get_url()[len("https://app.wandb.ai/"):])
model = run.file('agent.pt')
model.download(f"models/{experiment_name}/")
agent.load_state_dict(torch.load(f"models/{experiment_name}/agent.pt"))
agent.eval()
print(f"resumed at update {starting_update}")
for update in range(starting_update, num_updates + 1):
# Annealing the rate if instructed to do so.
if args.anneal_lr:
frac = 1.0 - (update - 1.0) / num_updates
lrnow = lr(frac)
optimizer.param_groups[0]['lr'] = lrnow
# TRY NOT TO MODIFY: prepare the execution of the game.
for step in range(0, args.num_steps):
envs.render()
global_step += 1 * args.num_envs
obs[step] = next_obs
dones[step] = next_done