def test_pendulum(self):
env = OpenAIGym(level='Pendulum', max_timesteps=100)
hidden_size = 16
actor_fn = (lambda: torch.nn.Sequential(
torch.nn.Linear(in_features=env.state_space['shape'][0],
out_features=hidden_size), torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_size,
out_features=env.action_space['shape'][0])))
critic_fn = (lambda: torch.nn.Sequential(
torch.nn.Linear(in_features=env.state_space['shape'][0],
out_features=hidden_size), torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_size, out_features=1)))
agent = AC(state_space=env.state_space,
action_space=env.action_space,
actor_fn=actor_fn,
actor_learning_rate=1e-3,
critic_fn=critic_fn,
critic_learning_rate=1e-3,
discount=0.95,
compute_advantage=True,
normalize_returns=True)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()
def test_cartpole(self):
env = OpenAIGym(level='CartPole', max_timesteps=100)
agent = RandomAgent(state_space=env.state_space,
action_space=env.action_space)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()
def test_frozenlake(self):
env = OpenAIGym(level='FrozenLake', max_timesteps=100)
agent = TQL(state_space=env.state_space,
action_space=env.action_space,
learning_rate=0.3)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()
def test_taxi(self):
env = OpenAIGym(level='Taxi', max_timesteps=100)
agent = TQL(state_space=env.state_space,
action_space=env.action_space,
learning_rate=0.3,
discount=0.95,
exploration=0.1)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()
def test_frozenlake(self):
env = OpenAIGym(level='FrozenLake', max_timesteps=100)
num_states = env.state_space['num_values']
hidden_size = 16
network_fn = (lambda: torch.nn.Sequential(
torch.nn.Embedding(num_embeddings=num_states, embedding_dim=hidden_size),
torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_size, out_features=env.action_space['num_values'])
))
agent = DQN(state_space=env.state_space, action_space=env.action_space, network_fn=network_fn,
learning_rate=1e-3)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()
def test_cartpole(self):
env = OpenAIGym(level='CartPole', max_timesteps=100)
hidden_size = 16
network_fn = (lambda: torch.nn.Sequential(
torch.nn.Linear(in_features=env.state_space['shape'][0], out_features=hidden_size),
torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_size, out_features=env.action_space['num_values'])
))
agent = DQN(state_space=env.state_space, action_space=env.action_space, network_fn=network_fn,
learning_rate=1e-3, target_network_update_frequency=10, memory=100, batch_size=16,
update_frequency=4)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()
def test_taxi(self):
env = OpenAIGym(level='Taxi', max_timesteps=100)
num_states = env.state_space['num_values']
hidden_size = 16
network_fn = (lambda: torch.nn.Sequential(
torch.nn.Embedding(num_embeddings=num_states, embedding_dim=hidden_size),
torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_size, out_features=env.action_space['num_values'])
))
agent = DQN(state_space=env.state_space, action_space=env.action_space, network_fn=network_fn,
learning_rate=1e-3, discount=0.95, exploration=0.1, target_network_update_frequency=10)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()
def test_cartpole(self):
env = OpenAIGym(level='CartPole', max_timesteps=100)
hidden_size = 16
actor_fn = (lambda: torch.nn.Sequential(
torch.nn.Linear(in_features=env.state_space['shape'][0],
out_features=hidden_size), torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_size,
out_features=env.action_space['num_values'])))
critic_fn = (lambda: torch.nn.Sequential(
torch.nn.Linear(in_features=env.state_space['shape'][0],
out_features=hidden_size), torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_size, out_features=1)))
agent = AC(state_space=env.state_space,
action_space=env.action_space,
actor_fn=actor_fn,
actor_learning_rate=1e-3,
critic_fn=critic_fn,
critic_learning_rate=1e-3)
experiment.train(agent=agent, env=env, num_episodes=10)
experiment.evaluate(agent=agent, env=env, num_episodes=10)
agent.close()
env.close()