Space Invaders with MADDPG

This tutorial shows how to train an MADDPG agent on the space invaders atari environment.

Atari Space Invaders

What is 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. For further information on MADDPG, check out the documentation.

Can I use it?

	Action	Observation
Discrete	✔️	✔️
Continuous	✔️	✔️

Code

Train multiple agents using MADDPG

The following code should run without any issues. The comments are designed to help you understand how to use PettingZoo with AgileRL. If you have any questions, please feel free to ask in the Discord server.

"""This tutorial shows how to train an MADDPG agent on the space invaders atari environment.

Authors: Michael (https://github.com/mikepratt1), Nick (https://github.com/nicku-a)
"""

import os
from copy import deepcopy

import numpy as np
import supersuit as ss
import torch
from pettingzoo.atari import space_invaders_v2
from tqdm import trange

from agilerl.components.multi_agent_replay_buffer import MultiAgentReplayBuffer
from agilerl.utils.utils import create_population
from agilerl.vector.pz_async_vec_env import AsyncPettingZooVecEnv

if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Define the network configuration
    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]
    }

    # Define the initial hyperparameters
    INIT_HP = {
        "POPULATION_SIZE": 1,
        "ALGO": "MADDPG",  # Algorithm
        # Swap image channels dimension from last to first [H, W, C] -> [C, H, W]
        "CHANNELS_LAST": True,
        "BATCH_SIZE": 32,  # Batch size
        "O_U_NOISE": True,  # Ornstein Uhlenbeck action noise
        "EXPL_NOISE": 0.1,  # Action noise scale
        "MEAN_NOISE": 0.0,  # Mean action noise
        "THETA": 0.15,  # Rate of mean reversion in OU noise
        "DT": 0.01,  # Timestep for OU noise
        "LR_ACTOR": 0.001,  # Actor learning rate
        "LR_CRITIC": 0.001,  # Critic learning rate
        "GAMMA": 0.95,  # Discount factor
        "MEMORY_SIZE": 100000,  # Max memory buffer size
        "LEARN_STEP": 100,  # Learning frequency
        "TAU": 0.01,  # For soft update of target parameters
    }

    num_envs = 8
    # Define the space invaders environment as a parallel environment
    env = space_invaders_v2.parallel_env()

    # Environment processing for image based observations
    env = ss.frame_skip_v0(env, 4)
    env = ss.clip_reward_v0(env, lower_bound=-1, upper_bound=1)
    env = ss.color_reduction_v0(env, mode="B")
    env = ss.resize_v1(env, x_size=84, y_size=84)
    env = ss.frame_stack_v1(env, 4)
    env = AsyncPettingZooVecEnv([lambda: env for _ in range(num_envs)])

    env.reset()

    # Configure the multi-agent algo input arguments
    try:
        state_dim = [env.single_observation_space(agent).n for agent in env.agents]
        one_hot = True
    except Exception:
        state_dim = [env.single_observation_space(agent).shape for agent in env.agents]
        one_hot = False
    try:
        action_dim = [env.single_action_space(agent).n for agent in env.agents]
        INIT_HP["DISCRETE_ACTIONS"] = True
        INIT_HP["MAX_ACTION"] = None
        INIT_HP["MIN_ACTION"] = None
    except Exception:
        action_dim = [env.single_action_space(agent).shape[0] for agent in env.agents]
        INIT_HP["DISCRETE_ACTIONS"] = False
        INIT_HP["MAX_ACTION"] = [
            env.single_action_space(agent).high for agent in env.agents
        ]
        INIT_HP["MIN_ACTION"] = [
            env.single_action_space(agent).low for agent in env.agents
        ]

    # Pre-process image dimensions for pytorch convolutional layers
    if INIT_HP["CHANNELS_LAST"]:
        state_dim = [
            (state_dim[2], state_dim[0], state_dim[1]) for state_dim in state_dim
        ]

    # Append number of agents and agent IDs to the initial hyperparameter dictionary
    INIT_HP["N_AGENTS"] = env.num_agents
    INIT_HP["AGENT_IDS"] = env.agents

    # Create a population ready for evolutionary hyper-parameter optimisation
    agent = create_population(
        INIT_HP["ALGO"],
        state_dim,
        action_dim,
        one_hot,
        NET_CONFIG,
        INIT_HP,
        population_size=INIT_HP["POPULATION_SIZE"],
        num_envs=num_envs,
        device=device,
    )[0]

    # Configure the multi-agent replay buffer
    field_names = ["state", "action", "reward", "next_state", "done"]
    memory = MultiAgentReplayBuffer(
        INIT_HP["MEMORY_SIZE"],
        field_names=field_names,
        agent_ids=INIT_HP["AGENT_IDS"],
        device=device,
    )

    # Define training loop parameters
    agent_ids = deepcopy(env.agents)
    max_steps = 20000  # Max steps (default: 2000000)
    learning_delay = 500  # Steps before starting learning
    training_steps = 10000  # Frequency at which we evaluate training score
    eval_steps = None  # Evaluation steps per episode - go until done
    eval_loop = 1  # Number of evaluation episodes

    total_steps = 0

    # TRAINING LOOP
    print("Training...")
    pbar = trange(max_steps, unit="step")
    while np.less(agent.steps[-1], max_steps):
        state, info = env.reset()  # Reset environment at start of episode
        scores = np.zeros((num_envs, len(agent_ids)))
        completed_episode_scores = []
        steps = 0
        if INIT_HP["CHANNELS_LAST"]:
            state = {
                agent_id: np.moveaxis(s, [-1], [-3]) for agent_id, s in state.items()
            }

        for idx_step in range(training_steps // num_envs):

            # 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
            action = {agent: env.action_space(agent).sample() for agent in env.agents}
            next_state, reward, termination, truncation, info = env.step(action)
            if not termination:
                assert False
            scores += np.array(list(reward.values())).transpose()
            total_steps += num_envs
            steps += num_envs

            # Image processing if necessary for the environment
            if INIT_HP["CHANNELS_LAST"]:
                next_state = {
                    agent_id: np.moveaxis(ns, [-1], [-3])
                    for agent_id, ns in next_state.items()
                }

            # Save experiences to replay buffer
            memory.save_to_memory(
                state,
                cont_actions,
                reward,
                next_state,
                termination,
                is_vectorised=True,
            )

            # Learn according to learning frequency
            # Handle learn steps > num_envs
            if agent.learn_step > num_envs:
                learn_step = agent.learn_step // num_envs
                if (
                    idx_step % learn_step == 0
                    and len(memory) >= agent.batch_size
                    and memory.counter > learning_delay
                ):
                    # Sample replay buffer
                    experiences = memory.sample(agent.batch_size)
                    # Learn according to agent's RL algorithm
                    agent.learn(experiences)
            # Handle num_envs > learn step; learn multiple times per step in env
            elif len(memory) >= agent.batch_size and memory.counter > learning_delay:
                for _ in range(num_envs // agent.learn_step):
                    # Sample replay buffer
                    experiences = memory.sample(agent.batch_size)
                    # Learn according to agent's RL algorithm
                    agent.learn(experiences)

            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)

        pbar.update(training_steps)

        agent.steps[-1] += steps

        # Evaluate population
        fitness = agent.test(
            env,
            swap_channels=INIT_HP["CHANNELS_LAST"],
            max_steps=eval_steps,
            loop=eval_loop,
            sum_scores=False,
        )
        pop_episode_scores = np.array(completed_episode_scores)
        mean_scores = np.mean(pop_episode_scores, axis=0)

        print(f"--- Global steps {total_steps} ---")
        print(f"Steps {agent.steps[-1]}")
        print("Scores:")
        for idx, sub_agent in enumerate(agent_ids):
            print(f"    {sub_agent} score: {mean_scores[idx]}")
        print("Fitness")
        for idx, sub_agent in enumerate(agent_ids):
            print(f"    {sub_agent} fitness: {fitness[idx]}")
        print("Previous 5 fitness avgs")
        for idx, sub_agent in enumerate(agent_ids):
            print(
                f"  {sub_agent} fitness average: {np.mean(agent.fitness[-5:], axis=0)[idx]}"
            )

        # Update step counter
        agent.steps.append(agent.steps[-1])

    # Save the trained algorithm
    path = "./models/MADDPG"
    filename = "MADDPG_trained_agent.pt"
    os.makedirs(path, exist_ok=True)
    save_path = os.path.join(path, filename)
    agent.save_checkpoint(save_path)

    pbar.close()
    env.close()

Watch the trained agents play

The following code allows you to load your saved MADDPG algorithm from the previous training block, test the algorithms performance, and then visualise a number of episodes as a gif.

import os

import imageio
import numpy as np
import supersuit as ss
import torch
from pettingzoo.atari import space_invaders_v2
from PIL import Image, ImageDraw

from agilerl.algorithms.maddpg import MADDPG


# Define function to return image
def _label_with_episode_number(frame, episode_num):
    im = Image.fromarray(frame)

    drawer = ImageDraw.Draw(im)

    if np.mean(frame) < 128:
        text_color = (255, 255, 255)
    else:
        text_color = (0, 0, 0)
    drawer.text(
        (im.size[0] / 20, im.size[1] / 18), f"Episode: {episode_num+1}", fill=text_color
    )

    return im


if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Configure the environment
    env = space_invaders_v2.parallel_env(render_mode="rgb_array")
    channels_last = True  # Needed for environments that use images as observations
    if channels_last:
        # Environment processing for image based observations
        env = ss.frame_skip_v0(env, 4)
        env = ss.clip_reward_v0(env, lower_bound=-1, upper_bound=1)
        env = ss.color_reduction_v0(env, mode="B")
        env = ss.resize_v1(env, x_size=84, y_size=84)
        env = ss.frame_stack_v1(env, 4)
    env.reset()
    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]

    # Pre-process image dimensions for pytorch convolutional layers
    if channels_last:
        state_dim = [
            (state_dim[2], state_dim[0], state_dim[1]) for state_dim in state_dim
        ]

    # Append number of agents and agent IDs to the initial hyperparameter dictionary
    n_agents = env.num_agents
    agent_ids = env.agents

    # Load the saved agent
    path = "./models/MADDPG/MADDPG_trained_agent.pt"
    maddpg = MADDPG.load(path, device)

    # Define test loop parameters
    episodes = 10  # Number of episodes to test agent on
    max_steps = 500  # Max number of steps to take in the environment in each episode

    rewards = []  # List to collect total episodic reward
    frames = []  # List to collect frames
    indi_agent_rewards = {
        agent_id: [] for agent_id in agent_ids
    }  # Dictionary to collect inidivdual agent rewards

    # Test loop for inference
    for ep in range(episodes):
        state, info = env.reset()
        agent_reward = {agent_id: 0 for agent_id in agent_ids}
        score = 0
        for _ in range(max_steps):
            if channels_last:
                state = {
                    agent_id: np.moveaxis(np.expand_dims(s, 0), [3], [1])
                    for agent_id, s in state.items()
                }
            # Get next action from agent
            cont_actions, discrete_action = maddpg.get_action(
                state, training=False, infos=info
            )
            if maddpg.discrete_actions:
                action = discrete_action
            else:
                action = cont_actions

            # Save the frame for this step and append to frames list
            frame = env.render()
            frames.append(_label_with_episode_number(frame, episode_num=ep))

            # Take action in environment
            state, reward, termination, truncation, info = env.step(
                {agent: a.squeeze() for agent, a in action.items()}
            )

            # Save agent's reward for this step in this episode
            for agent_id, r in reward.items():
                agent_reward[agent_id] += r

            # Determine total score for the episode and then append to rewards list
            score = sum(agent_reward.values())

            # Stop episode if any agents have terminated
            if any(truncation.values()) or any(termination.values()):
                break

        rewards.append(score)

        # Record agent specific episodic reward for each agent
        for agent_id in agent_ids:
            indi_agent_rewards[agent_id].append(agent_reward[agent_id])

        print("-" * 15, f"Episode: {ep}", "-" * 15)
        print("Episodic Reward: ", rewards[-1])
        for agent_id, reward_list in indi_agent_rewards.items():
            print(f"{agent_id} reward: {reward_list[-1]}")
    env.close()

    # Save the gif to specified path
    gif_path = "./videos/"
    os.makedirs(gif_path, exist_ok=True)
    imageio.mimwrite(
        os.path.join("./videos/", "space_invaders.gif"), frames, duration=10
    )