score, steps_taken, collision = 0, 0, False
# Build initial observations for each agent
for a in range(flags.num_agents):
agent_obs[a] = normalize_observation(
obs[a], flags.tree_depth, zero_center=flags.agent_type == 'dqn')
agent_obs_buffer[a] = agent_obs[a].copy()
# Run an episode
for step in range(max_steps):
update_values = [False] * flags.num_agents
action_dict = {}
for a in range(flags.num_agents):
if info['action_required'][a]:
action_dict[a] = agent.act(agent_obs[a], eps=eps)
# action_dict[a] = np.random.randint(5)
update_values[a] = True
steps_taken += 1
else:
action_dict[a] = 0
# Environment step
obs, rewards, done, info = env.step(action_dict)
score += sum(rewards.values()) / flags.num_agents
# Check for collisions and episode completion
if step == max_steps - 1:
done['__all__'] = True
if any(is_collision(a) for a in obs):
collision = True
lib_dir = os.path.abspath(os.path.join(path, os.pardir))
sys.path.insert(1,lib_dir)
from dqn.agent import Agent
ENV_NAME = 'Humanoid-v2'
env = gym.make(ENV_NAME)
np.random.seed(0)
env.seed(0)
nb_actions = 17
agent = Agent(state_size=376, action_size=17, seed=0)
agent.qnetwork_local.load_state_dict(torch.load('checkpoint.pth'))
for i in range(15):
state = env.reset()
while True:
env.render()
action = agent.act(state)
state, reward, done, _ = env.step(action)
print(reward)
if done:
break
print("Iteration ",i)
env.close()
