AgileRL: Implementing MADDPG#
This tutorial shows how to train an MADDPG agent on the space invaders atari environment.
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 AgileRL documentation.
Can I use it?#
Action Space |
Observation Space |
|
|---|---|---|
Discrete |
✔️ |
✔️ |
Continuous |
✔️ |
✔️ |
Environment Setup#
To follow this tutorial, you will need to install the dependencies shown below. It is recommended to use a newly-created virtual environment to avoid dependency conflicts.
agilerl>=0.1.16
pettingzoo[classic,atari,mpe]>=1.23.1
SuperSuit>=3.9.0
torch>=2.0.1
numpy>=1.24.2
tqdm>=4.65.0
fastrand==1.3.0
gymnasium>=0.28.1
imageio>=2.31.1
Pillow>=9.5.0
PyYAML>=5.4.1
wandb>=0.13.10
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
import numpy as np
import supersuit as ss
import torch
from agilerl.components.multi_agent_replay_buffer import MultiAgentReplayBuffer
from agilerl.hpo.mutation import Mutations
from agilerl.hpo.tournament import TournamentSelection
from agilerl.utils.utils import initialPopulation
from tqdm import trange
from pettingzoo.atari import space_invaders_v2
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("===== AgileRL MADDPG Demo =====")
# Define the network configuration
NET_CONFIG = {
"arch": "cnn", # Network architecture
"h_size": [32, 32], # Network hidden size
"c_size": [3, 32], # CNN channel size
"k_size": [(1, 3, 3), (1, 3, 3)], # CNN kernel size
"s_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": 2,
"ALGO": "MADDPG", # Algorithm
# Swap image channels dimension from last to first [H, W, C] -> [C, H, W]
"CHANNELS_LAST": True,
"BATCH_SIZE": 8, # Batch size
"LR": 0.01, # Learning rate
"GAMMA": 0.95, # Discount factor
"MEMORY_SIZE": 10000, # Max memory buffer size
"LEARN_STEP": 5, # Learning frequency
"TAU": 0.01, # For soft update of target parameters
}
# Define the space invaders environment as a parallel environment
env = space_invaders_v2.parallel_env()
if INIT_HP["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()
# 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]
INIT_HP["DISCRETE_ACTIONS"] = True
INIT_HP["MAX_ACTION"] = None
INIT_HP["MIN_ACTION"] = None
except Exception:
action_dim = [env.action_space(agent).shape[0] for agent in env.agents]
INIT_HP["DISCRETE_ACTIONS"] = False
INIT_HP["MAX_ACTION"] = [env.action_space(agent).high for agent in env.agents]
INIT_HP["MIN_ACTION"] = [env.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
pop = initialPopulation(
INIT_HP["ALGO"],
state_dim,
action_dim,
one_hot,
NET_CONFIG,
INIT_HP,
population_size=INIT_HP["POPULATION_SIZE"],
device=device,
)
# 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,
)
# Instantiate a tournament selection object (used for HPO)
tournament = TournamentSelection(
tournament_size=2, # Tournament selection size
elitism=True, # Elitism in tournament selection
population_size=INIT_HP["POPULATION_SIZE"], # Population size
evo_step=1,
) # Evaluate using last N fitness scores
# Instantiate a mutations object (used for HPO)
mutations = Mutations(
algo=INIT_HP["ALGO"],
no_mutation=0.2, # Probability of no mutation
architecture=0.2, # Probability of architecture mutation
new_layer_prob=0.2, # Probability of new layer mutation
parameters=0.2, # Probability of parameter mutation
activation=0, # Probability of activation function mutation
rl_hp=0.2, # Probability of RL hyperparameter mutation
rl_hp_selection=[
"lr",
"learn_step",
"batch_size",
], # RL hyperparams selected for mutation
mutation_sd=0.1, # Mutation strength
# Define search space for each hyperparameter
min_lr=0.0001,
max_lr=0.01,
min_learn_step=1,
max_learn_step=120,
min_batch_size=8,
max_batch_size=64,
agent_ids=INIT_HP["AGENT_IDS"], # Agent IDs
arch=NET_CONFIG["arch"], # MLP or CNN
rand_seed=1,
device=device,
)
# Define training loop parameters
max_episodes = 5 # Total episodes (default: 6000)
max_steps = 900 # Maximum steps to take in each episode
epsilon = 1.0 # Starting epsilon value
eps_end = 0.1 # Final epsilon value
eps_decay = 0.995 # Epsilon decay
evo_epochs = 20 # Evolution frequency
evo_loop = 1 # Number of evaluation episodes
elite = pop[0] # Assign a placeholder "elite" agent
# Training loop
for idx_epi in trange(max_episodes):
for agent in pop: # Loop through population
state, info = env.reset() # Reset environment at start of episode
agent_reward = {agent_id: 0 for agent_id in env.agents}
if INIT_HP["CHANNELS_LAST"]:
state = {
agent_id: np.moveaxis(np.expand_dims(s, 0), [-1], [-3])
for agent_id, s in state.items()
}
for _ in range(max_steps):
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.getAction(
state, epsilon, agent_mask, env_defined_actions
)
if agent.discrete_actions:
action = discrete_action
else:
action = cont_actions
next_state, reward, termination, truncation, info = env.step(
action
) # Act in environment
# Image processing if necessary for the environment
if INIT_HP["CHANNELS_LAST"]:
state = {agent_id: np.squeeze(s) for agent_id, s in state.items()}
next_state = {
agent_id: np.moveaxis(ns, [-1], [-3])
for agent_id, ns in next_state.items()
}
# Save experiences to replay buffer
memory.save2memory(state, cont_actions, reward, next_state, termination)
# Collect the reward
for agent_id, r in reward.items():
agent_reward[agent_id] += r
# Learn according to learning frequency
if (memory.counter % agent.learn_step == 0) and (
len(memory) >= agent.batch_size
):
experiences = memory.sample(
agent.batch_size
) # Sample replay buffer
agent.learn(experiences) # Learn according to agent's RL algorithm
# Update the state
if INIT_HP["CHANNELS_LAST"]:
next_state = {
agent_id: np.expand_dims(ns, 0)
for agent_id, ns in next_state.items()
}
state = next_state
# Stop episode if any agents have terminated
if any(truncation.values()) or any(termination.values()):
break
# Save the total episode reward
score = sum(agent_reward.values())
agent.scores.append(score)
# Update epsilon for exploration
epsilon = max(eps_end, epsilon * eps_decay)
# Now evolve population if necessary
if (idx_epi + 1) % evo_epochs == 0:
# Evaluate population
fitnesses = [
agent.test(
env,
swap_channels=INIT_HP["CHANNELS_LAST"],
max_steps=max_steps,
loop=evo_loop,
)
for agent in pop
]
print(f"Episode {idx_epi + 1}/{max_episodes}")
print(f'Fitnesses: {["%.2f" % fitness for fitness in fitnesses]}')
print(
f'100 fitness avgs: {["%.2f" % np.mean(agent.fitness[-100:]) for agent in pop]}'
)
# Tournament selection and population mutation
elite, pop = tournament.select(pop)
pop = mutations.mutation(pop)
# 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)
elite.saveCheckpoint(save_path)
Watch the trained agents play#
The following code allows you to load your saved MADDPG alogorithm 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 agilerl.algorithms.maddpg import MADDPG
from PIL import Image, ImageDraw
from pettingzoo.atari import space_invaders_v2
# 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
# Instantiate an MADDPG object
maddpg = MADDPG(
state_dim,
action_dim,
one_hot,
n_agents,
agent_ids,
max_action,
min_action,
discrete_actions,
device=device,
)
# Load the saved algorithm into the MADDPG object
path = "./models/MADDPG/MADDPG_trained_agent.pt"
maddpg.loadCheckpoint(path)
# 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()
}
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 = maddpg.getAction(
state,
epsilon=0,
agent_mask=agent_mask,
env_defined_actions=env_defined_actions,
)
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(action)
# 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
)