Cross-Entropy Method (CEM)¶

Algorithm¶

Algorithm implementation¶

Main notation/symbols:

- policy function approximator (\(\pi_\theta\))
- states (\(s\)), actions (\(a\)), rewards (\(r\)), next states (\(s'\)), dones (\(d\))
- loss (\(L\))

Decision making¶

act(...)
\(a \leftarrow \pi_\theta(s)\)

Learning algorithm¶

_update(...)
# sample all memory
\(s, a, r, s', d \leftarrow\) states, actions, rewards, next_states, dones
# compute discounted return threshold
\([G] \leftarrow \sum_{t=0}^{E-1}\) discount_factor\(^{t} \, r_t\) for each episode
\(G_{_{bound}} \leftarrow q_{th_{quantile}}([G])\) at the given percentile
# get elite states and actions
\(s_{_{elite}} \leftarrow s[G \geq G_{_{bound}}]\)
\(a_{_{elite}} \leftarrow a[G \geq G_{_{bound}}]\)
# compute scores for the elite states
\(scores \leftarrow \theta(s_{_{elite}})\)
# compute policy loss
\(L_{\pi_\theta} \leftarrow -\sum_{i=1}^{N} a_{_{elite}} \log(scores)\)
# optimization step
reset \(\text{optimizer}_\theta\)
\(\nabla_{\theta} L_{\pi_\theta}\)
step \(\text{optimizer}_\theta\)
# update learning rate
IF there is a learning_rate_scheduler THEN
step \(\text{scheduler}_\theta (\text{optimizer}_\theta)\)

Usage¶

# import the agent and its default configuration
from skrl.agents.torch.cem import CEM, CEM_DEFAULT_CONFIG

# instantiate the agent's models
models = {}
models["policy"] = ...

# adjust some configuration if necessary
cfg_agent = CEM_DEFAULT_CONFIG.copy()
cfg_agent["<KEY>"] = ...

# instantiate the agent
# (assuming a defined environment <env> and memory <memory>)
agent = CEM(models=models,
            memory=memory,  # only required during training
            cfg=cfg_agent,
            observation_space=env.observation_space,
            action_space=env.action_space,
            device=env.device)

# import the agent and its default configuration
from skrl.agents.jax.cem import CEM, CEM_DEFAULT_CONFIG

# instantiate the agent's models
models = {}
models["policy"] = ...

# adjust some configuration if necessary
cfg_agent = CEM_DEFAULT_CONFIG.copy()
cfg_agent["<KEY>"] = ...

# instantiate the agent
# (assuming a defined environment <env> and memory <memory>)
agent = CEM(models=models,
            memory=memory,  # only required during training
            cfg=cfg_agent,
            observation_space=env.observation_space,
            action_space=env.action_space,
            device=env.device)

Configuration and hyperparameters¶

CEM_DEFAULT_CONFIG = {
    "rollouts": 16,                 # number of rollouts before updating
    "percentile": 0.70,             # percentile to compute the reward bound [0, 1]

    "discount_factor": 0.99,        # discount factor (gamma)

    "learning_rate": 1e-2,          # learning rate
    "learning_rate_scheduler": None,        # learning rate scheduler class (see torch.optim.lr_scheduler)
    "learning_rate_scheduler_kwargs": {},   # learning rate scheduler's kwargs (e.g. {"step_size": 1e-3})

    "state_preprocessor": None,             # state preprocessor class (see skrl.resources.preprocessors)
    "state_preprocessor_kwargs": {},        # state preprocessor's kwargs (e.g. {"size": env.observation_space})

    "random_timesteps": 0,          # random exploration steps
    "learning_starts": 0,           # learning starts after this many steps

    "rewards_shaper": None,         # rewards shaping function: Callable(reward, timestep, timesteps) -> reward

    "experiment": {
        "directory": "",            # experiment's parent directory
        "experiment_name": "",      # experiment name
        "write_interval": 250,      # TensorBoard writing interval (timesteps)

        "checkpoint_interval": 1000,        # interval for checkpoints (timesteps)
        "store_separately": False,          # whether to store checkpoints separately

        "wandb": False,             # whether to use Weights & Biases
        "wandb_kwargs": {}          # wandb kwargs (see https://docs.wandb.ai/ref/python/init)
    }
}

Spaces¶

The implementation supports the following Gym spaces / Gymnasium spaces

Gym/Gymnasium spaces	Observation	Action
Discrete	\(\square\)	\(\blacksquare\)
MultiDiscrete	\(\square\)	\(\blacksquare\)
Box	\(\blacksquare\)	\(\square\)
Dict	\(\blacksquare\)	\(\square\)

Models¶

The implementation uses 1 discrete function approximator. This function approximator (model) must be collected in a dictionary and passed to the constructor of the class under the argument models

Notation	Concept	Key	Input shape	Output shape	Type
\(\pi(s)\)	Policy	`"policy"`	observation	action	Categorical / Multi-Categorical

Features¶

Support for advanced features is described in the next table

Feature	Support and remarks
RNN support	-	\(\square\)	\(\square\)

API (PyTorch)¶

skrl.agents.torch.cem.CEM_DEFAULT_CONFIG¶: alias of {‘discount_factor’: 0.99, ‘experiment’: {‘checkpoint_interval’: 1000, ‘directory’: ‘’, ‘experiment_name’: ‘’, ‘store_separately’: False, ‘wandb’: False, ‘wandb_kwargs’: {}, ‘write_interval’: 250}, ‘learning_rate’: 0.01, ‘learning_rate_scheduler’: None, ‘learning_rate_scheduler_kwargs’: {}, ‘learning_starts’: 0, ‘percentile’: 0.7, ‘random_timesteps’: 0, ‘rewards_shaper’: None, ‘rollouts’: 16, ‘state_preprocessor’: None, ‘state_preprocessor_kwargs’: {}}

Bases: Agent

Cross-Entropy Method (CEM)

https://ieeexplore.ieee.org/abstract/document/6796865/

Parameters:

models (dictionary of skrl.models.torch.Model) – Models used by the agent
memory (skrl.memory.torch.Memory, list of skrl.memory.torch.Memory or None) – Memory to storage the transitions. If it is a tuple, the first element will be used for training and for the rest only the environment transitions will be added
observation_space (int, tuple or list of int, gym.Space, gymnasium.Space or None, optional) – Observation/state space or shape (default: None)
action_space (int, tuple or list of int, gym.Space, gymnasium.Space or None, optional) – Action space or shape (default: None)
device (str or torch.device, optional) – Device on which a tensor/array is or will be allocated (default: None). If None, the device will be either "cuda" if available or "cpu"
cfg (dict) – Configuration dictionary

Raises:

KeyError – If the models dictionary is missing a required key

_update(timestep: int, timesteps: int) → None¶

Algorithm’s main update step

Parameters:

timestep (int) – Current timestep
timesteps (int) – Number of timesteps

act(states: torch.Tensor, timestep: int, timesteps: int) → torch.Tensor¶

Process the environment’s states to make a decision (actions) using the main policy

Parameters:

states (torch.Tensor) – Environment’s states
timestep (int) – Current timestep
timesteps (int) – Number of timesteps

Returns:

Actions

Return type:

torch.Tensor

init(trainer_cfg: Mapping[str, Any] | None = None) → None¶: Initialize the agent

post_interaction(timestep: int, timesteps: int) → None¶

Callback called after the interaction with the environment

Parameters:

timestep (int) – Current timestep
timesteps (int) – Number of timesteps

pre_interaction(timestep: int, timesteps: int) → None¶

Callback called before the interaction with the environment

Parameters:

timestep (int) – Current timestep
timesteps (int) – Number of timesteps

record_transition(states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, next_states: torch.Tensor, terminated: torch.Tensor, truncated: torch.Tensor, infos: Any, timestep: int, timesteps: int) → None¶

Record an environment transition in memory

Parameters:

states (torch.Tensor) – Observations/states of the environment used to make the decision
actions (torch.Tensor) – Actions taken by the agent
rewards (torch.Tensor) – Instant rewards achieved by the current actions
next_states (torch.Tensor) – Next observations/states of the environment
terminated (torch.Tensor) – Signals to indicate that episodes have terminated
truncated (torch.Tensor) – Signals to indicate that episodes have been truncated
infos (Any type supported by the environment) – Additional information about the environment
timestep (int) – Current timestep
timesteps (int) – Number of timesteps

API (JAX)¶

skrl.agents.jax.cem.CEM_DEFAULT_CONFIG¶: alias of {‘discount_factor’: 0.99, ‘experiment’: {‘checkpoint_interval’: 1000, ‘directory’: ‘’, ‘experiment_name’: ‘’, ‘store_separately’: False, ‘wandb’: False, ‘wandb_kwargs’: {}, ‘write_interval’: 250}, ‘learning_rate’: 0.01, ‘learning_rate_scheduler’: None, ‘learning_rate_scheduler_kwargs’: {}, ‘learning_starts’: 0, ‘percentile’: 0.7, ‘random_timesteps’: 0, ‘rewards_shaper’: None, ‘rollouts’: 16, ‘state_preprocessor’: None, ‘state_preprocessor_kwargs’: {}}

Bases: Agent

Cross-Entropy Method (CEM)

https://ieeexplore.ieee.org/abstract/document/6796865/

Parameters:

models (dictionary of skrl.models.jax.Model) – Models used by the agent
memory (skrl.memory.jax.Memory, list of skrl.memory.jax.Memory or None) – Memory to storage the transitions. If it is a tuple, the first element will be used for training and for the rest only the environment transitions will be added
observation_space (int, tuple or list of int, gym.Space, gymnasium.Space or None, optional) – Observation/state space or shape (default: None)
action_space (int, tuple or list of int, gym.Space, gymnasium.Space or None, optional) – Action space or shape (default: None)
device (str or jax.Device, optional) – Device on which a tensor/array is or will be allocated (default: None). If None, the device will be either "cuda" if available or "cpu"
cfg (dict) – Configuration dictionary

Raises:

KeyError – If the models dictionary is missing a required key

_update(timestep: int, timesteps: int) → None¶

Algorithm’s main update step

Parameters:

timestep (int) – Current timestep
timesteps (int) – Number of timesteps

act(states: ndarray | jax.Array, timestep: int, timesteps: int) → ndarray | jax.Array¶

Process the environment’s states to make a decision (actions) using the main policy

Parameters:

states (np.ndarray or jax.Array) – Environment’s states
timestep (int) – Current timestep
timesteps (int) – Number of timesteps

Returns:

Actions

Return type:

np.ndarray or jax.Array

init(trainer_cfg: Mapping[str, Any] | None = None) → None¶: Initialize the agent

post_interaction(timestep: int, timesteps: int) → None¶

Callback called after the interaction with the environment

Parameters:

timestep (int) – Current timestep
timesteps (int) – Number of timesteps

pre_interaction(timestep: int, timesteps: int) → None¶

Callback called before the interaction with the environment

Parameters:

timestep (int) – Current timestep
timesteps (int) – Number of timesteps

Record an environment transition in memory

Parameters:

states (np.ndarray or jax.Array) – Observations/states of the environment used to make the decision
actions (np.ndarray or jax.Array) – Actions taken by the agent
rewards (np.ndarray or jax.Array) – Instant rewards achieved by the current actions
next_states (np.ndarray or jax.Array) – Next observations/states of the environment
terminated (np.ndarray or jax.Array) – Signals to indicate that episodes have terminated
truncated (np.ndarray or jax.Array) – Signals to indicate that episodes have been truncated
infos (Any type supported by the environment) – Additional information about the environment
timestep (int) – Current timestep
timesteps (int) – Number of timesteps