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	Observation
Discrete	✔️	✔️
Continuous	✔️	✔️

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. This can be defined by your environment, and should be returned in ‘info’ as a dictionary. Info must contain two dictionaries - one named ‘agent_mask’, which contains a boolean value for whether an action should be returned for each agent, and another named ‘env_defined_actions’, which contains the actions for each agent that a new action is not generated for. This is handled automatically by the AgileRL multi-agent training function, but can be implemented in a custom loop as follows:

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

state, info = env.reset()  # or: next_state, reward, done, truncation, info = env.step(action)
cont_actions, discrete_action = agent.get_action(state, epsilon, info['agent_mask'], info['env_defined_actions'])
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.components.multi_agent_replay_buffer import MultiAgentReplayBuffer
from agilerl.wrappers.pettingzoo_wrappers import PettingZooVectorizationParallelWrapper

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
num_envs = 8
env = simple_speaker_listener_v4.parallel_env(max_cycles=25, continuous_actions=True)
env = PettingZooVectorizationParallelWrapper(env, n_envs=num_envs)
env.reset()

# Configure the multi-agent algo input arguments
try:
    state_dim = [env.observation_space(agent).n for agent in env.agents]
    one_hot = True
except Exception:
    state_dim = [env.observation_space(agent).shape for agent in env.agents]
    one_hot = False
try:
    action_dim = [env.action_space(agent).n for agent in env.agents]
    discrete_actions = True
    max_action = None
    min_action = None
except Exception:
    action_dim = [env.action_space(agent).shape[0] for agent in env.agents]
    discrete_actions = False
    max_action = [env.action_space(agent).high for agent in env.agents]
    min_action = [env.action_space(agent).low 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(
    state_dims=state_dim,
    action_dims=action_dim,
    one_hot=one_hot,
    n_agents=n_agents,
    agent_ids=agent_ids,
    max_action=max_action,
    min_action=min_action,
    vect_noise_dim=num_envs,
    discrete_actions=discrete_actions,
    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: np.moveaxis(s, [-1], [-3]) for agent_id, s in state.items()}

    for _ in range(1000):
        agent_mask = info["agent_mask"] if "agent_mask" in info.keys() else None
        env_defined_actions = (
            info["env_defined_actions"]
            if "env_defined_actions" in info.keys()
            else None
        )

        # Get next action from agent
        cont_actions, discrete_action = agent.get_action(
            states=state,
            training=True,
            agent_mask=agent_mask,
            env_defined_actions=env_defined_actions,
        )
        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: np.moveaxis(ns, [-1], [-3])
                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 network architecture, pass a kwargs dict to the MADDPG net_config field. Full arguments can be found in the documentation of EvolvableMLP and EvolvableCNN. For an MLP, this can be as simple as:

NET_CONFIG = {
      'arch': 'mlp',      # Network architecture
      'hidden_size': [32, 32]  # Network hidden size
  }

Or for a CNN:

NET_CONFIG = {
      'arch': 'cnn',      # Network architecture
      'hidden_size': [32,32],    # Network hidden size
      'channel_size': [32, 32], # CNN channel size
      'kernel_size': [3, 3],   # CNN kernel size
      'stride_size': [2, 2],   # CNN stride size
      'normalize': True   # Normalize image from range [0,255] to [0,1]
  }

agent = MADDPG(state_dims=state_dim,
               action_dims=action_dim,
               one_hot=one_hot,
               n_agents=n_agents,
               agent_ids=agent_ids,
               max_action=max_action,
               min_action=min_action,
               discrete_actions=discrete_actions,
               net_config=NET_CONFIG)   # Create MADDPG agent

Saving and loading agents

To save an agent, use the save_checkpoint method:

from agilerl.algorithms.maddpg import MADDPG

agent = MADDPG(state_dims=state_dim,
               action_dims=action_dim,
               one_hot=one_hot,
               n_agents=n_agents,
               agent_ids=agent_ids,
               max_action=max_action,
               min_action=min_action,
               discrete_actions=discrete_actions)   # Create MADDPG agent

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(state_dims, action_dims, one_hot, n_agents, agent_ids, max_action, min_action, discrete_actions, O_U_noise=True, expl_noise=0.1, vect_noise_dim=1, mean_noise=0.0, theta=0.15, dt=0.01, index=0, net_config={'arch': 'mlp', 'hidden_size': [64, 64]}, batch_size=64, lr_actor=0.001, lr_critic=0.01, learn_step=5, gamma=0.95, tau=0.01, mut=None, actor_networks=None, critic_networks=None, device='cpu', accelerator=None, wrap=True)

The MADDPG algorithm class. MADDPG paper: https://arxiv.org/abs/1706.02275

Parameters:

• state_dims (list[tuple]) – State observation dimensions for each agent

• action_dims (list[int]) – Action dimensions for each agent

• one_hot (bool) – One-hot encoding, used with discrete observation spaces

• n_agents (int) – Number of agents

• agent_ids (list[str]) – Agent ID for each agent

• max_action (list[float]) – Upper bound of the action space for each agent

• min_action (list[float]) – Lower bound of the action space for each agent

• discrete_actions (bool, optional) – Boolean flag to indicate a discrete action space

• 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

• net_config (dict, optional) – Network 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

• mutation (str, optional) – Most recent mutation to agent, defaults to None

• 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

• wrap (bool, optional) – Wrap models for distributed training upon creation, defaults to True

action_noise(idx)

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:

np.ndArray

clone(index=None, wrap=True)

Returns cloned agent identical to self.

Parameters:

index (int, optional) – Index to keep track of agent for tournament selection and mutation, defaults to None

get_action(states, training=True, agent_mask=None, env_defined_actions=None)

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:

• state (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

• agent_mask (Dict[str, bool]) – Mask of agents to return actions for: {‘agent_0’: True, …, ‘agent_n’: False}

• env_defined_actions (Dict[str, np.array]) – Dictionary of actions defined by the environment: {‘agent_0’: np.array, …, ‘agent_n’: np.array}

learn(experiences)

Updates agent network parameters to learn from experiences.

Parameters:

experience – Tuple of dictionaries containing batched states, actions, rewards, next_states,

dones in that order for each individual agent. :type experience: Tuple[Dict[str, torch.Tensor]]

classmethod load(path, device='cpu', accelerator=None)

Creates agent with properties and network weights loaded from path.

Parameters:

• path (string) – Location to load checkpoint from

• device (str, optional) – Device for accelerated computing, ‘cpu’ or ‘cuda’, defaults to ‘cpu’

• accelerator (accelerate.Accelerator(), optional) – Accelerator for distributed computing, defaults to None

load_checkpoint(path)

Loads saved agent properties and network weights from checkpoint.

Parameters:

path (string) – Location to load checkpoint from

reset_action_noise(indices)

Reset action noise.

save_checkpoint(path)

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

Parameters:

path (string) – Location to save checkpoint at

scale_to_action_space(action, idx)

Scales actions to action space defined by self.min_action and self.max_action.

Parameters:

action (numpy.ndarray) – Action to be scaled

soft_update(net, target)

Soft updates target network.

test(env, swap_channels=False, max_steps=None, loop=3)

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