import gym
from agent import DDPG
env = gym.make('Pendulum-v0')
agent = DDPG(env)
agent.load_model()
state = env.reset()
cumulative_reward = 0
for i in range(200):
action = agent.get_action(state)
env.render()
state, reward, _, _ = env.step(action * 2)
cumulative_reward += reward
print('Cumulative Reward: {}'.format(cumulative_reward))
from collections import deque
import gym
import numpy as np
from agent import DDPG
from utils import get_screen
env = gym.make('Pendulum-v0')
agent = DDPG(env, memory=False)
agent.load_model()
env.reset()
pixel = env.render(mode='rgb_array')
state = deque([get_screen(pixel) for _ in range(3)], maxlen=3)
cumulative_reward = 0
for timestep in range(200):
action = agent.get_action(np.array(state)[np.newaxis])
_, reward, _, _ = env.step(action * 2)
pixel = env.render(mode='rgb_array')
state_ = state.copy()
state_.append(get_screen(pixel))
state = state_
cumulative_reward += reward
print('Cumulative Reward: {}'.format(cumulative_reward))
np.random.seed(42)
env.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed(42)
writer = SummaryWriter(log_dir='logs/')
agent = DDPG(env, writer)
all_timesteps = 0
for e in range(epoch):
noise = OUActionNoise(env.action_space.shape[0])
state = env.reset()
cumulative_reward = 0
for timestep in range(200):
action = agent.get_action(state, noise, timestep)
state_, reward, done, _ = env.step(action * env.action_space.high[0])
# env.render()
agent.store_transition(state, action, state_, reward, done)
state = state_
cumulative_reward += reward
agent.update(all_timesteps)
all_timesteps += 1
print('Epoch : {} / {}, Cumulative Reward : {}'.format(
e, epoch, cumulative_reward))
writer.add_scalar("reward", cumulative_reward, e)
agent.save_model()
torch.manual_seed(42)
torch.cuda.manual_seed(42)
writer = SummaryWriter(log_dir='logs/')
agent = DDPG(env, writer)
all_timesteps = 0
for e in range(epoch):
noise = OUActionNoise(env.action_space.shape[0])
env.reset()
pixel = env.render(mode='rgb_array')
state = deque([get_screen(pixel) for _ in range(3)], maxlen=3)
cumulative_reward = 0
for timestep in range(200):
action = agent.get_action(np.array(state)[np.newaxis], noise, timestep)
_, reward, done, _ = env.step(action * env.action_space.high[0])
pixel = env.render(mode='rgb_array')
state_ = state.copy()
state_.append(get_screen(pixel))
agent.store_transition(np.array(state), action, np.array(state_),
reward, done)
state = state_
cumulative_reward += reward
agent.update(all_timesteps, batch_size=16)
all_timesteps += 1
print('Epoch : {} / {}, Cumulative Reward : {}'.format(
e, epoch, cumulative_reward))
writer.add_scalar("reward", cumulative_reward, e)