def main(model_path, n_test_episodes):
run_paths = glob.glob(os.path.join(model_path, '*'))
for run_path in run_paths:
if len(glob.glob(os.path.join(run_path, 'carracing_results*'))) > 0:
print(run_path, 'already processed')
continue
# Load run config
run_config = json.load(open(os.path.join(run_path, 'config.json'), 'r'))
env = gym.make("CarRacing-v0").unwrapped
num_actions = 5
# Define networks and load agent
if run_config['model'] == 'Resnet':
Q_net = ResnetVariant(num_actions=num_actions, history_length=run_config['history_length'] + 1).to(device)
Q_target_net = ResnetVariant(num_actions=num_actions, history_length=run_config['history_length'] + 1).to(
device)
elif run_config['model'] == 'Lenet':
Q_net = LeNetVariant(num_actions=num_actions, history_length=run_config['history_length'] + 1).to(device)
Q_target_net = LeNetVariant(num_actions=num_actions, history_length=run_config['history_length'] + 1).to(
device)
elif run_config['model'] == 'DeepQNetwork':
Q_net = DeepQNetwork(num_actions=num_actions, history_length=run_config['history_length'] + 1).to(device)
Q_target_net = DeepQNetwork(num_actions=num_actions, history_length=run_config['history_length'] + 1).to(
device)
else:
raise ValueError('{} not implmented.'.format(run_config['model']))
agent = DQNAgent(Q=Q_net, Q_target=Q_target_net, num_actions=num_actions, **run_config)
agent.load(os.path.join(run_path, 'agent.pt'))
episode_rewards = []
for i in range(n_test_episodes):
stats = run_episode(env, agent, deterministic=True, history_length=run_config['history_length'],
do_training=False, rendering=True, normalize_images=run_config['normalize_images'],
skip_frames=run_config['skip_frames'], max_timesteps=1000)
episode_rewards.append(stats.episode_reward)
# save results in a dictionary and write them into a .json file
results = dict()
results["episode_rewards"] = episode_rewards
results["mean"] = np.array(episode_rewards).mean()
results["std"] = np.array(episode_rewards).std()
fname = "{}/carracing_results_dqn-{}.json".format(run_path, datetime.now().strftime("%Y%m%d-%H%M%S"))
fh = open(fname, "w")
json.dump(results, fh)
fh.close()
env.close()
print('... finished')
np.random.seed(0)
if __name__ == "__main__":
env = gym.make("CartPole-v0").unwrapped
# TODO: load DQN agent
# ...
state_dim = 4
num_actions = 2
Q = MLP(state_dim, num_actions)
Q_target = MLP(state_dim, num_actions)
agent = DQNAgent(Q, Q_target, num_actions)
agent.load("C:\\Users\\Monish\\Desktop\\workspace\\exercise3_R\\reinforcement_learning\\models_cartpole\\dqn_agent_199.pt")
n_test_episodes = 15
episode_rewards = []
for i in range(n_test_episodes):
stats = run_episode(env, agent, deterministic=True, do_training=False, rendering=True)
episode_rewards.append(stats.episode_reward)
# save results in a dictionary and write them into a .json file
results = dict()
results["episode_rewards"] = episode_rewards
results["mean"] = np.array(episode_rewards).mean()
results["std"] = np.array(episode_rewards).std()
if not os.path.exists("./results"):
def setup_ai(model_path):
agent = DQNAgent(STATE_SIZE, N_ACTIONS, N_AGENTS, None, None, None)
agent.epsilon = 0.01
agent.load(model_path)
return DQNAgentWrapper(agent, STACK_SIZE)
np.random.seed(0)
if __name__ == "__main__":
env = gym.make("CartPole-v0").unwrapped
# TODO: load DQN agent
# ...
states_dim = 4
action_dim = 2
Q = MLP(states_dim, action_dim)
Q_target = MLP(states_dim, action_dim)
agent = DQNAgent(Q, Q_target, action_dim, double=True)
agent.load("./models_cartpole/dqn_agent_fixed_1.pt")
n_test_episodes = 15
episode_rewards = []
for i in range(n_test_episodes):
stats = run_episode(env,
agent,
deterministic=True,
do_training=False,
rendering=True)
episode_rewards.append(stats.episode_reward)
# save results in a dictionary and write them into a .json file
results = dict()
results["episode_rewards"] = episode_rewards
results["mean"] = np.array(episode_rewards).mean()
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", type=str, help="Model file to use", required=True)
parser.add_argument('-e', "--episodes", type=int, help="num episodes to try", default=5, required=False)
args = parser.parse_args()
env = gym.make("CarRacing-v0").unwrapped
history_length = 5
#TODO: Define networks and load agent
# ....
Q_network = CNN(history_length=history_length, n_classes=5)
Q_target = CNN(history_length=history_length, n_classes=5)
agent = DQNAgent(Q=Q_network, Q_target=Q_target, num_actions=5)
agent.load(args.model)
episode_rewards = []
for i in range(args.episodes):
stats = run_episode(env, agent, deterministic=True, do_training=False, rendering=True, history_length=history_length)
episode_rewards.append(stats.episode_reward)
print('Episode %d - [ Reward %.2f ]' % (i+1, stats.episode_reward))
# save results in a dictionary and write them into a .json file
results = dict()
results["episode_rewards"] = episode_rewards
results["mean"] = np.array(episode_rewards).mean()
results["std"] = np.array(episode_rewards).std()
if not os.path.exists("./results"):
os.mkdir("./results")
def setup_ai(model_path):
agent = DQNAgent(STATE_SIZE, N_ACTIONS, N_AGENTS, EPSILON, None, None,
0.95)
agent.load(model_path)
return DQNAgentWrapper(agent, STACK_SIZE)
def setup_ai(model_path):
agent = DQNAgent(STATE_SIZE, N_ACTIONS, N_AGENTS, EPSILON, None, None, 0.95)
agent.load(model_path)
return DQNAgentWrapper(agent, STACK_SIZE)
from agent.networks import *
import numpy as np
from agent.networks import MLP
import torch
np.random.seed(0)
if __name__ == "__main__":
env = gym.make("CartPole-v0").unwrapped
# TODO: load DQN agent
# ...
Q = MLP(state_dim = 4,action_dim = 2)
Q_target = MLP(state_dim = 4, action_dim = 2)
agent = DQNAgent(Q, Q_target, num_actions = 2)
agent.load("./models_cartpole/dqn_agent-perfect.pt")
n_test_episodes = 15
episode_rewards = []
for i in range(n_test_episodes):
stats = run_episode(env, agent, deterministic=True, do_training=False, rendering=True,epsilon=0)
episode_rewards.append(stats.episode_reward)
# save results in a dictionary and write them into a .json file
results = dict()
results["episode_rewards"] = episode_rewards
results["mean"] = np.array(episode_rewards).mean()
results["std"] = np.array(episode_rewards).std()
if not os.path.exists("./results"):
os.mkdir("./results")