Multi-Agent Deep Deterministic Policy Gradient (MADDPG)¶

MADDPG (Multi-Agent Deep Deterministic Policy Gradients) extends the DDPG (Deep Deterministic Policy Gradients) algorithm to enable cooperative or competitive training of multiple agents in complex environments, enhancing the stability and convergence of the learning process through decentralized actor and centralized critic architectures.

MADDPG paper: https://arxiv.org/pdf/1706.02275.pdf

Can I use it?¶

Action Space¶

`Discrete`	`Box`	`MultiDiscrete`	`MultiBinary`
❌	✔️	❌	❌

Gumbel-Softmax¶

The Gumbel-Softmax activation function is a differentiable approximation that enables gradient-based optimization through continuous relaxation of discrete action spaces in multi-agent reinforcement learning, allowing agents to learn and improve decision-making in complex environments with discrete choices. If you would like to customise the mlp output activation function, you can define it within the network configuration using the key “output_activation”. User definition for the output activation is however, unnecessary, as the algorithm will select the appropriate function given the environments action space.

Agent Masking¶

If you need to take actions from agents at different timesteps, you can use agent masking to only retrieve new actions for certain agents whilst providing ‘environment defined actions’ for other agents, which act as a nominal action for such “masked” agents to take. These nominal actions should be returned as part of the info dictionary. Following the PettingZoo API we recommend the info dictionary to be keyed by the agents, with env_defined_actions defined as follows:

info = {'speaker_0': {'env_defined_actions':  None},
        'listener_0': {'env_defined_actions': np.array([0,0,0,0,0])}

For agents that you wish not to be masked, the env_defined_actions should be set to None. If your environment has discrete action spaces then provide ‘env_defined_actions’ as a numpy array with a single value. For example, an action space of type Discrete(5) may have an env_defined_action of np.array([4]). For an environment with continuous actions spaces (e.g. Box(0, 1, (5,))) then the shape of the array should be the size of the action space (np.array([0.5, 0.5, 0.5, 0.5, 0.5])). Agent masking is handled automatically by the AgileRL multi-agent training function by passing the info dictionary into the agents get_action method:

state, info = env.reset()  # or: next_state, reward, done, truncation, info = env.step(action)
cont_actions, discrete_action = agent.get_action(state, infos=info)
if agent.discrete_actions:
    action = discrete_action
else:
    action = cont_actions

Example¶

import numpy as np
import torch
from pettingzoo.mpe import simple_speaker_listener_v4
from tqdm import trange

from agilerl.algorithms import MADDPG
from agilerl.components.multi_agent_replay_buffer import MultiAgentReplayBuffer
from agilerl.vector.pz_async_vec_env import AsyncPettingZooVecEnv
from agilerl.utils.algo_utils import obs_channels_to_first

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
num_envs = 8
env = AsyncPettingZooVecEnv(
    [
        lambda: simple_speaker_listener_v4.parallel_env(continuous_actions=True)
        for _ in range(num_envs)
    ]
)
env.reset()

# Configure the multi-agent algo input arguments
observation_spaces = [env.single_observation_space(agent) for agent in env.agents]
action_spaces = [env.single_action_space(agent) for agent in env.agents]

channels_last = False  # Swap image channels dimension from last to first [H, W, C] -> [C, H, W]
n_agents = env.num_agents
agent_ids = [agent_id for agent_id in env.agents]
field_names = ["state", "action", "reward", "next_state", "done"]
memory = MultiAgentReplayBuffer(
    memory_size=1_000_000,
    field_names=field_names,
    agent_ids=agent_ids,
    device=device,
)

agent = MADDPG(
    observation_spaces=observation_spaces,
    action_spaces=action_spaces,
    agent_ids=agent_ids,
    vect_noise_dim=num_envs,
    device=device,
)

# Define training loop parameters
max_steps = 100000  # Max steps
total_steps = 0
while agent.steps[-1] < max_steps:
    state, info  = env.reset() # Reset environment at start of episode
    scores = np.zeros(num_envs)
    completed_episode_scores = []
    if channels_last:
        state = {agent_id: obs_channels_to_first(s) for agent_id, s in state.items()}

    for _ in range(1000):

        # Get next action from agent
        cont_actions, discrete_action = agent.get_action(
            states=state,
            training=True,
            infos=info,
        )
        if agent.discrete_actions:
            action = discrete_action
        else:
            action = cont_actions

        # Act in environment
        next_state, reward, termination, truncation, info = env.step(action)

        scores += np.sum(np.array(list(reward.values())).transpose(), axis=-1)
        total_steps += num_envs
        steps += num_envs

        # Save experiences to replay buffer
        if channels_last:
            next_state = {
                agent_id: obs_channels_to_first(ns)
                for agent_id, ns in next_state.items()
            }
        memory.save_to_memory(state, cont_actions, reward, next_state, done, is_vectorised=True)

        # Learn according to learning frequency
        if len(memory) >= agent.batch_size:
            for _ in range(num_envs // agent.learn_step):
                experiences = memory.sample(agent.batch_size) # Sample replay buffer
                agent.learn(experiences) # Learn according to agent's RL algorithm

        # Update the state
        state = next_state

        # Calculate scores and reset noise for finished episodes
        reset_noise_indices = []
        term_array = np.array(list(termination.values())).transpose()
        trunc_array = np.array(list(truncation.values())).transpose()
        for idx, (d, t) in enumerate(zip(term_array, trunc_array)):
            if np.any(d) or np.any(t):
                completed_episode_scores.append(scores[idx])
                agent.scores.append(scores[idx])
                scores[idx] = 0
                reset_noise_indices.append(idx)
        agent.reset_action_noise(reset_noise_indices)

    agent.steps[-1] += steps

Neural Network Configuration¶

To configure the architecture of the network’s encoder / head, pass a kwargs dict to the MADDPG net_config field. Full arguments can be found in the documentation of EvolvableMLP, EvolvableCNN, and EvolvableMultiInput.

For discrete / vector observations:

NET_CONFIG = {
      "encoder_config": {'hidden_size': [32, 32]},  # Network head hidden size
      "head_config": {'hidden_size': [32]}      # Network head hidden size
  }

For image observations:

NET_CONFIG = {
    "encoder_config": {
      'channel_size': [32, 32], # CNN channel size
      'kernel_size': [8, 4],   # CNN kernel size
      'stride_size': [4, 2],   # CNN stride size
    },
    "head_config": {'hidden_size': [32]}  # Network head hidden size
  }

For dictionary / tuple observations containing any combination of image, discrete, and vector observations:

CNN_CONFIG = {
    "channel_size": [32, 32], # CNN channel size
    "kernel_size": [8, 4],   # CNN kernel size
    "stride_size": [4, 2],   # CNN stride size
}

NET_CONFIG = {
    "encoder_config": {
      "latent_dim": 32,
      # Config for nested EvolvableCNN objects
      "cnn_config": CNN_CONFIG,
      # Config for nested EvolvableMLP objects
      "mlp_config": {
          "hidden_size": [32, 32]
      },
      "vector_space_mlp": True # Process vector observations with an MLP
    },
    "head_config": {'hidden_size': [32]}  # Network head hidden size
  }

# Create MADDPG agent
agent = MADDPG(
  observation_spaces=observation_spaces,
  action_spaces=action_spaces,
  agent_ids=agent_ids,
  net_config=NET_CONFIG,
  device=device,
)

Evolutionary Hyperparameter Optimization¶

AgileRL allows for efficient hyperparameter optimization during training to provide state-of-the-art results in a fraction of the time. For more information on how this is done, please refer to the Evolutionary Hyperparameter Optimization documentation.

Saving and loading agents¶

To save an agent, use the save_checkpoint method:

from agilerl.algorithms.maddpg import MADDPG

# Create MADDPG agent
agent = MADDPG(
  observation_spaces=observation_spaces,
  action_spaces=action_spaces,
  agent_ids=agent_ids,
  net_config=NET_CONFIG,
  device=device,
)

checkpoint_path = "path/to/checkpoint"
agent.save_checkpoint(checkpoint_path)

To load a saved agent, use the load method:

from agilerl.algorithms.maddpg import MADDPG

checkpoint_path = "path/to/checkpoint"
agent = MADDPG.load(checkpoint_path)

Parameters¶

class agilerl.algorithms.maddpg.MADDPG(*args, **kwargs)¶

Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.

Paper: https://arxiv.org/abs/1706.02275

Parameters:

observation_spaces (list[spaces.Space]) – Observation space for each agent
action_spaces (list[spaces.Space]) – Action space for each agent
agent_ids (list[str]) – Agent ID for each agent
O_U_noise (bool, optional) – Use Ornstein Uhlenbeck action noise for exploration. If False, uses Gaussian noise. Defaults to True
vect_noise_dim (int, optional) – Vectorization dimension of environment for action noise, defaults to 1
expl_noise (float, optional) – Scale for Ornstein Uhlenbeck action noise, or standard deviation for Gaussian exploration noise
mean_noise (float, optional) – Mean of exploration noise, defaults to 0.0
theta (float, optional) – Rate of mean reversion in Ornstein Uhlenbeck action noise, defaults to 0.15
dt (float, optional) – Timestep for Ornstein Uhlenbeck action noise update, defaults to 1e-2
index (int, optional) – Index to keep track of object instance during tournament selection and mutation, defaults to 0
hp_config (HyperparameterConfig, optional) – RL hyperparameter mutation configuration, defaults to None, whereby algorithm mutations are disabled.
net_config (dict, optional) – Encoder configuration, defaults to mlp with hidden size [64,64]
batch_size (int, optional) – Size of batched sample from replay buffer for learning, defaults to 64
lr_actor (float, optional) – Learning rate for actor optimizer, defaults to 0.001
lr_critic (float, optional) – Learning rate for critic optimizer, defaults to 0.01
learn_step (int, optional) – Learning frequency, defaults to 5
gamma (float, optional) – Discount factor, defaults to 0.95
tau (float, optional) – For soft update of target network parameters, defaults to 0.01
mut (str, optional) – Most recent mutation to agent, defaults to None
normalize_images (bool, optional) – Normalize image observations, defaults to True
actor_networks (list[nn.Module], optional) – List of custom actor networks, defaults to None
critic_networks (list[nn.Module], optional) – List of custom critic networks, defaults to None
device (str, optional) – Device for accelerated computing, ‘cpu’ or ‘cuda’, defaults to ‘cpu’
accelerator (accelerate.Accelerator(), optional) – Accelerator for distributed computing, defaults to None
torch_compiler (str, optional) – The torch compile mode ‘default’, ‘reduce-overhead’ or ‘max-autotune’, defaults to None
wrap (bool, optional) – Wrap models for distributed training upon creation, defaults to True

action_noise(idx: int) → Tensor¶

Create action noise for exploration, either Ornstein Uhlenbeck or: from a normal distribution.

Parameters:: idx (int) – Agent index for action dims
Returns:: Action noise
Return type:: torch.Tensor

assemble_homogeneous_outputs(agent_outputs: Dict[str, ndarray], vect_dim: int) → Dict[str, ndarray]¶

Assembles individual agent outputs into batched outputs for shared policies.

Parameters:

agent_outputs (Dict[str, np.ndarray]) – Dictionary with individual agent outputs, e.g. {‘agent_0’: 4, ‘agent_1’: 7, ‘agent_2’: 8}
vect_dim (int) – Vectorization dimension size, i.e. number of vect envs

Returns:

Assembled dictionary with the form {‘agent’: [4, 7, 8]}

Return type:

Dict[str, np.ndarray]

clone(index: int | None = None, wrap: bool = True) → SelfEvolvableAlgorithm¶

Creates a clone of the algorithm.

Parameters:

index (Optional[int], optional) – The index of the clone, defaults to None
wrap (bool, optional) – If True, wrap the models in the clone with the accelerator, defaults to False

Returns:

A clone of the algorithm

Return type:

EvolvableAlgorithm

static copy_attributes(agent: SelfEvolvableAlgorithm, clone: SelfEvolvableAlgorithm) → SelfEvolvableAlgorithm¶

Copies the non-evolvable attributes of the algorithm to a clone.

Parameters:: clone (SelfEvolvableAlgorithm) – The clone of the algorithm.
Returns:: The clone of the algorithm.
Return type:: SelfEvolvableAlgorithm

disassemble_homogeneous_outputs(homo_outputs: Dict[str, ndarray], vect_dim: int) → Dict[str, ndarray]¶

Disassembles batched output by shared policies into their homogeneous agents’ outputs.

Parameters:

homo_outputs (Dict[str, np.ndarray]) – Dictionary to be disassembled, has the form {‘agent’: [4, 7, 8]}
vect_dim (int) – Vectorization dimension size, i.e. number of vect envs

Returns:

Assembled dictionary, e.g. {‘agent_0’: 4, ‘agent_1’: 7, ‘agent_2’: 8}

Return type:

Dict[str, np.ndarray]

evolvable_attributes(networks_only: bool = False) → Dict[str, EvolvableModule | List[EvolvableModule] | Optimizer | Iterable[Optimizer] | OptimizerWrapper]¶

Returns the attributes related to the evolvable networks in the algorithm. Includes attributes that are either evolvable networks or a list of evolvable networks, as well as the optimizers associated with the networks.

Parameters:: networks_only (bool, optional) – If True, only include evolvable networks, defaults to False
Returns:: A dictionary of network attributes.
Return type:: dict[str, Any]

extract_action_masks(infos: Dict[str, Dict[str, Any]]) → Dict[str, ndarray]¶

Extract action masks from info dictionary

Parameters:: infos (Dict[str, Dict[...]]) – Info dict
Returns:: Action masks
Return type:: Dict[str, np.ndarray]

extract_agent_masks(infos: Dict[str, Dict[str, Any]]) → Tuple[Dict[str, ndarray], Dict[str, ndarray]]¶

Extract env_defined_actions from info dictionary and determine agent masks

Parameters:: infos (Dict[str, Dict[...]]) – Info dict

Returns the next action to take in the environment. Epsilon is the probability of taking a random action, used for exploration. For epsilon-greedy behaviour, set epsilon to 0.

Parameters:

obs (Dict[str, numpy.Array]) – Environment observations: {‘agent_0’: state_dim_0, …, ‘agent_n’: state_dim_n}
training (bool, optional) – Agent is training, use exploration noise, defaults to True
infos (Dict[str, Dict[str, ...]]) – Information dictionary returned by env.step(actions)

Returns:

Tuple of actions for each agent

Return type:

Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray]]

Returns the dimension of the action space as it pertains to the underlying networks (i.e. the output size of the networks).

Parameters:: action_space (spaces.Space or List[spaces.Space].) – The action space of the environment.
Returns:: The dimension of the action space.
Return type:: int.

get_homo_id(agent_id: str) → str¶

Get the homogeneous ID for an agent.

Parameters:: agent_id (str) – The agent ID
Returns:: The homogeneous ID

get_lr_names() → List[str]¶: Returns the learning rates of the algorithm.

get_policy() → EvolvableModule¶: Returns the policy network of the algorithm.

Returns the dimension of the state space as it pertains to the underlying networks (i.e. the input size of the networks).

Parameters:: observation_space (spaces.Space or List[spaces.Space].) – The observation space of the environment.
Returns:: The dimension of the state space.
Return type:: Tuple[int, …].

property index: int¶: Returns the index of the algorithm.

static inspect_attributes(agent: SelfEvolvableAlgorithm, input_args_only: bool = False) → Dict[str, Any]¶

Inspect and retrieve the attributes of the current object, excluding attributes related to the underlying evolvable networks (i.e. EvolvableModule, torch.optim.Optimizer) and with an option to include only the attributes that are input arguments to the constructor.

Parameters:: input_args_only (bool) – If True, only include attributes that are input arguments to the constructor. Defaults to False.
Returns:: A dictionary of attribute names and their values.
Return type:: dict[str, Any]

Updates agent network parameters to learn from experiences.

Parameters:: experience (Tuple[Dict[str, torch.Tensor]]) – Tuple of dictionaries containing batched states, actions, rewards, next_states, dones in that order for each individual agent.
Returns:: Loss dictionary
Return type:: Dict[str, torch.Tensor]

classmethod load(path: str, device: str | device = 'cpu', accelerator: Accelerator | None = None) → SelfEvolvableAlgorithm¶

Loads an algorithm from a checkpoint.

Parameters:

path (string) – Location to load checkpoint from.
device (str, optional) – Device to load the algorithm on, defaults to ‘cpu’
accelerator (Optional[Accelerator], optional) – Accelerator object for distributed computing, defaults to None

Returns:

An instance of the algorithm

Return type:

RLAlgorithm

load_checkpoint(path: str) → None¶

Loads saved agent properties and network weights from checkpoint.

Parameters:: path (string) – Location to load checkpoint from

property mut: Any¶: Returns the mutation object of the algorithm.

mutation_hook() → None¶: Executes the hooks registered with the algorithm.

classmethod population(size: int, observation_space: Box | Discrete | MultiDiscrete | Dict | Tuple | MultiBinary | List[Box | Discrete | MultiDiscrete | Dict | Tuple | MultiBinary], action_space: Box | Discrete | MultiDiscrete | Dict | Tuple | MultiBinary | List[Box | Discrete | MultiDiscrete | Dict | Tuple | MultiBinary], wrapper_cls: Type[SelfAgentWrapper] | None = None, wrapper_kwargs: Dict[str, Any] = {}, **kwargs) → List[SelfEvolvableAlgorithm | SelfAgentWrapper]¶

Creates a population of algorithms.

Parameters:: size (int.) – The size of the population.
Returns:: A list of algorithms.
Return type:: List[SelfEvolvableAlgorithm].

Preprocesses observations for forward pass through neural network.

Parameters:: observations (numpy.ndarray[float] or dict[str, numpy.ndarray[float]]) – Observations of environment
Returns:: Preprocessed observations
Return type:: torch.Tensor[float] or dict[str, torch.Tensor[float]] or Tuple[torch.Tensor[float], …]

process_infos(infos: Dict[str, Dict[str, Any]] | None) → Tuple[Dict[str, ndarray], Dict[str, ndarray], Dict[str, ndarray]]¶

Process the information, extract env_defined_actions, action_masks and agent_masks

Parameters:: infos (Dict[str, Dict[...]]) – Info dict
Returns:: Tuple of action masks, env_defined_actions and agent masks
Return type:: Tuple[ArrayDict, ArrayDict, ArrayDict]

recompile() → None¶: Recompiles the evolvable modules in the algorithm with the specified torch compiler.

register_mutation_hook(hook: Callable) → None¶

Registers a hook to be executed after a mutation is performed on the algorithm.

Parameters:: hook (Callable) – The hook to be executed after mutation.

register_network_group(group: NetworkGroup) → None¶

Sets the evaluation network for the algorithm.

Parameters:: name (str) – The name of the evaluation network.

reset_action_noise(indices: List[int]) → None¶: Reset action noise.

save_checkpoint(path: str) → None¶

Saves a checkpoint of agent properties and network weights to path.

Parameters:: path (string) – Location to save checkpoint at

set_training_mode(training: bool) → None¶

Sets the training mode of the algorithm.

Parameters:: training (bool) – If True, set the algorithm to training mode.

soft_update(net: Module, target: Module) → None¶

Soft updates target network.

Parameters:

net (nn.Module) – Network to be updated
target (nn.Module) – Target network

Process observations for critic network input.

Note

Assumes that the observation spaces for the different agents are the same.

Parameters:: obs (Dict[str, torch.Tensor]) – Observation dict
Returns:: Stacked observations
Return type:: torch.Tensor

sum_shared_rewards(rewards: Dict[str, ndarray]) → Dict[str, ndarray]¶

Sums the rewards for homogeneous agents

Parameters:: rewards (Dict[str, np.ndarray]) – Reward dictionary from environment
Returns:: Summed rewards dictionary
Return type:: Dict[str, np.ndarray]

test(env: str | Env | VectorEnv, swap_channels: bool = False, max_steps: int | None = None, loop: int = 3, sum_scores: bool = True) → float¶

Returns mean test score of agent in environment with epsilon-greedy policy.

Parameters:

env (Gym-style environment) – The environment to be tested in
swap_channels (bool, optional) – Swap image channels dimension from last to first [H, W, C] -> [C, H, W], defaults to False
max_steps (int, optional) – Maximum number of testing steps, defaults to None
loop (int, optional) – Number of testing loops/episodes to complete. The returned score is the mean. Defaults to 3
sum_scores (book, optional) – Boolean flag to indicate whether to sum sub-agent scores, defaults to True

Returns:

Mean test score

Return type:

float

Moves experiences to the device.

Parameters:: experiences (Tuple[torch.Tensor[float], ...]) – Experiences to move to device
Returns:: Experiences on the device
Return type:: Tuple[torch.Tensor[float], …]

unwrap_models()¶: Unwraps the models in the algorithm from the accelerator.

wrap_models() → None¶: Wraps the models in the algorithm with the accelerator.