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.
Compatible Action Spaces¶
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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 agilerl.algorithms.maddpg import MADDPG
from agilerl.components.multi_agent_replay_buffer import MultiAgentReplayBuffer
from agilerl.utils.utils import (
create_population,
default_progress_bar,
make_multi_agent_vect_envs,
)
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define the network configuration
NET_CONFIG = {
"latent_dim": 128,
"encoder_config": {
"channel_size": [32, 32], # CNN channel size
"kernel_size": [3, 3], # CNN kernel size
"stride_size": [1, 1], # CNN stride size
},
"head_config": {"hidden_size": [128]}, # Actor head hidden size
}
# Define the initial hyperparameters
INIT_HP = {
"POPULATION_SIZE": 1,
"ALGO": "MADDPG", # Algorithm
"BATCH_SIZE": 128, # 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": 0.0001, # Actor learning rate
"LR_CRITIC": 0.001, # Critic learning rate
"GAMMA": 0.95, # Discount factor
"MEMORY_SIZE": 100_000, # Max memory buffer size
"LEARN_STEP": 50, # Learning frequency
"TAU": 0.01, # For soft update of target parameters
}
num_envs = 8
# Define the space invaders environment as a parallel environment
def make_env():
env = space_invaders_v2.parallel_env()
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)
return ss.reshape_v0(env, (4, 84, 84))
# Environment processing for image based observations
env = make_multi_agent_vect_envs(env=make_env, num_envs=num_envs)
# 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]
# Append number of agents and agent IDs to the initial hyperparameter dictionary
INIT_HP["AGENT_IDS"] = env.agents
# Create a population ready for evolutionary hyper-parameter optimisation
agent: MADDPG = create_population(
INIT_HP["ALGO"],
observation_spaces,
action_spaces,
NET_CONFIG,
INIT_HP,
population_size=INIT_HP["POPULATION_SIZE"],
num_envs=num_envs,
device=device,
)[0]
agent.set_training_mode(True)
# Configure the multi-agent replay buffer
field_names = ["obs", "action", "reward", "next_obs", "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 = 2_000_000 # Max steps (default: 2000000)
learning_delay = 500 # Steps before starting learning
training_steps = 10_000 # 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 = default_progress_bar(max_steps)
while np.less(agent.steps[-1], max_steps):
obs, info = env.reset() # Reset environment at start of episode
scores = np.zeros((num_envs, len(agent_ids)))
completed_episode_scores = []
steps = 0
for idx_step in range(training_steps // num_envs):
# Get next action from agent and take a step in the environment
action, raw_action = agent.get_action(obs=obs, infos=info)
next_obs, reward, termination, truncation, info = env.step(action)
scores += np.array(list(reward.values())).transpose()
total_steps += num_envs
steps += num_envs
pbar.update(num_envs)
# Save experiences to replay buffer
memory.save_to_memory(
obs,
raw_action,
reward,
next_obs,
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)
obs = next_obs
# 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, strict=False)):
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
# Evaluate population
fitness = agent.test(
env,
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)
pbar.write(
f"--- Global steps {total_steps} ---\n"
f"Steps {agent.steps[-1]}\n"
f"Scores: {np.mean(mean_scores)}\n"
f"Fitnesses: {np.mean(fitness)}\n"
f"5 fitness avgs: {np.mean(agent.fitness[-5:])}",
)
# 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 import MADDPG
from agilerl.utils.algo_utils import obs_channels_to_first
# Define function to return image
def _label_with_episode_number(frame, episode_num):
im = Image.fromarray(frame)
drawer = ImageDraw.Draw(im)
text_color = (255, 255, 255) if np.mean(frame) < 128 else (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()
# Append number of agents and agent IDs to the initial hyperparameter dictionary
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):
obs, info = env.reset()
agent_reward = dict.fromkeys(agent_ids, 0)
score = 0
for _ in range(max_steps):
if channels_last:
obs = {
agent_id: obs_channels_to_first(s) for agent_id, s in obs.items()
}
# Get next action from agent
action, _ = maddpg.get_action(obs, infos=info)
# 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
obs, 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,
)