def main():
agent = Agent()
agent.load()
total_reward = 0
obs = env.reset()
env.render()
for _ in range(10000):
act = agent.predict(obs)
obs, reward, done, _ = env.step(act)
total_reward += reward
env.render()
if done:
print(f'total_reward: {total_reward}')
env.close()
break
] # a, b = [deque([]), deque([])]
agent_obs = [None] * flags.num_agents # [None, None]
agent_obs_buffer = [None] * flags.num_agents
agent_action_buffer = [2] * flags.num_agents
max_steps = flags.episode_length
start_time = time.time()
# Load an RL agent and initialize it from checkpoint if necessary
# independent dqn/ppo -->每个人obs不同，同一个model
if flags.agent_type == "dqn":
agent = DQN_Agent(state_size, action_size, flags.num_agents)
elif flags.agent_type == "ppo":
agent = PPO_Agent(state_size, action_size, flags.num_agents)
if flags.load_model:
start, eps = agent.load(project_root / 'checkpoints', 0, 1.0)
else:
start, eps = 0, 1
if not flags.train:
eps = 0.0
# Helper function to detect collisions
ACTIONS = {0: "up", 1: "right", 2: "down", 3: "left", 4: "stop"}
def obs_wrapper(obss):
'''
utility: [list] -> [array]
'''
return np.array(obss)
crop_start = (15, 30)
crop_end = (200, 125)
starting_epsilon = 0.05 if LOAD_MODEL else 1.0
env = gym.make('SpaceInvaders-v0')
brain = Agent(gamma=0.95,
epsilon=0.05,
lr=0.003,
input_dims=input_dims,
batch_size=batch_size,
n_actions=n_actions,
max_mem_size=5000,
save_path='models/')
if LOAD_MODEL:
brain.load()
else:
# load memory with random games
while brain.mem_cntr < brain.mem_size:
observation = env.reset()
observation = preprocess(observation, crop_start, crop_end)
done = False
while not done:
# 0 no action, 1 fire, 2 move right, 3 move left, 4 move right fire, 5 move left fire
action = env.action_space.sample()
observation_, reward, done, info = env.step(action)
observation_ = preprocess(observation_, crop_start, crop_end)
if done and info['ale.lives'] == 0:
reward = -100
brain.store_transition(observation, action, reward, observation_,
int(done))
env.render()
while True:
action = self.agent.act(states)
states, _, done, _ = env.step(action)
env.render()
if done:
break
time.sleep(0.01)
seed = 3721
env = SingleLngEnv(
n_loc=10,
n_steps=1000,
fuel_cost=0.1,
price_sigma=0.1,
price_daily_vol=0.02,
price_theta=0.01,
max_distance=30.0,
normalize=True
)
# from ddpg import Agent
from dqn import Agent
agent = Agent(state_size=env.n_loc * 3 + 2, action_size=env.n_loc, random_seed=seed)
agent.load()
agent.train(env, 1000, 1000)
solver = DdpgLngSolver(env, agent, 3721)
solver.solve()
