'mu': mu_hat
}.items():
plt.pcolormesh(p,
s,
zz.T,
cmap='coolwarm_r',
vmin=-1,
vmax=1,
rasterized=True)
plt.xlabel(r'$p$')
plt.ylabel(r'$s$')
if 'q' in key:
plt.colorbar()
plt.savefig(
f'4_{generator.__name__}_{stop}_{method.__name__}_{key}.pdf'
)
plt.close()
### Compute J^mû_N'
N_prime = math.ceil(math.log((eps / B_r), gamma))
trajectories = samples(policify(mu_hat), N_prime)
j_hat = expected_return(trajectories, N_prime)
print('J^mû_N =', j_hat)
print()
def main(
render: bool = False,
eps: float = 1.0,
n_actions: int = 11,
n_estimators: int = 20
):
# Environment
gym.logger.set_level(40)
env = gym.make('InvertedDoublePendulumPyBulletEnv-v0')
# Rendering
if render:
env.render()
env.reset()
# /!\ Only work in our modified version of PyBullet Gym
env.camera.env._p.resetDebugVisualizerCamera(2, 0, -20, [0, 0, 0])
# Setup
gamma = 0.95
max_steps = 1000
n_evaluate = 20 # number of episodes to evaluate model
B_r = 10 # maximum possible reward
N = math.ceil(math.log((eps / (2 * B_r)) * (1. - gamma) ** 2, gamma))
print(f'N = {N}')
rewards = []
# Discrete actions
actions = np.linspace(
env.action_space.low[0],
env.action_space.high[0],
n_actions
)
# Agent
agent = FQI(
U=actions,
U_dim=env.action_space.shape[0],
gamma=gamma,
n_estimators=n_estimators
)
agent.fill(env, 3000, max_steps) # generate 3000 transitions to start
# Training
for _ in tqdm(range(N)):
# Train the model
agent.optimize()
# Evaluate
evals = []
for _ in range(n_evaluate):
x = env.reset()
cr = 0 # cumulative reward
for step in range(max_steps):
u = agent.action(x)
x_prime, r, done, _ = env.step(u)
# Store the transition in agent's transitions list
agent.memory.append((x, u[0], r, x_prime, done))
x = x_prime
if done:
break
cr += (gamma ** step) * r
evals.append(cr)
print(f'Memory size: {len(agent.memory)}')
rewards.append(evals)
# Export results
rewards = np.array(rewards)
mean = np.mean(rewards, axis=1)
std = np.std(rewards, axis=1)
plt.plot(mean)
plt.fill_between(
range(N),
mean - std,
mean + std,
alpha=0.3
)
plt.xlabel('N')
plt.ylabel(r'$J^{\mu}$')
plt.savefig(f'fqi_J_{n_actions}_{n_estimators}.pdf')
plt.close()
def main(render: bool = False,
n_episodes: int = 500,
discrete: int = None,
n_layers: int = 1,
gamma: float = 0.95,
activation_id: str = 'relu'):
# Environment
gym.logger.set_level(40)
env = gym.make('InvertedDoublePendulumPyBulletEnv-v0')
# Rendering
if render:
env.render()
env.reset()
# /!\ Only work in our modified version of PyBullet Gym
env.camera.env._p.resetDebugVisualizerCamera(2, 0, -20, [0, 0, 0])
# Setup
max_steps = 1000
n_evaluate = 50 # number of episodes to evaluate model
rewards = []
# Agent
activations = {'relu': nn.ReLU, 'elu': nn.ELU}
activation = activations.get(activation_id)
agent = DDPG(env,
gamma=gamma,
discrete=discrete,
n_layers=n_layers,
activation=activation)
noise = OrnsteinUhlenbeck(env.action_space)
# Training
for _ in tqdm(range(n_episodes)):
x = env.reset()
noise.reset()
# Simulate the episode until terminal state or max number of steps
for step in range(max_steps):
u = agent.action(x)
if discrete is None:
u = noise.action(u)
x_prime, r, done, _ = env.step(u)
# Save transition
agent.memory.push((torch.tensor(x).float(), u[0], r,
torch.tensor(x_prime).float(), done))
# Optimization
if agent.memory.is_ready():
agent.optimize()
x = x_prime
# If terminal state, stop the episode
if done:
break
# Evaluation
evals = []
for _ in range(n_evaluate):
x = env.reset()
cr = 0 # cumulative reward
for step in range(max_steps):
u = agent.action(x)
x, r, done, _ = env.step(u)
if done:
break
cr += (gamma**step) * r
evals.append(cr)
rewards.append(evals)
# Export results
rewards = np.array(rewards)
mean = np.mean(rewards, axis=1)
std = np.std(rewards, axis=1)
plt.plot(mean)
plt.fill_between(range(n_episodes), mean - std, mean + std, alpha=0.3)
plt.xlabel('Episode')
plt.ylabel(r'$J^{\mu}$')
plt.savefig(f'ddpg_J_{discrete}_{n_layers}_{gamma}.pdf')
plt.close()