Pros of Baselines

• Comprehensive collection of RL algorithms
• Well-established and widely used in research
• Extensive documentation and community support

Cons of Baselines

• Complex codebase with interdependencies
• Less focus on readability and simplicity
• Slower development and updates

Code Comparison

Baselines (PPO implementation):

def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
            vf_coef=0.5,  max_grad_norm=0.5, gamma=0.99, lam=0.95,
            log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
            save_interval=0, load_path=None, model_fn=None, update_fn=None, init_fn=None, mpi_rank_weight=1, comm=None, **network_kwargs):

CleanRL (PPO implementation):

def train(env_id, num_envs, learning_rate, seed, total_timesteps, torch_deterministic, cuda, track, wandb_project_name, wandb_entity, capture_video, save_model):

CleanRL offers a more streamlined and readable implementation, focusing on simplicity and ease of understanding. Baselines provides a more feature-rich but complex implementation with numerous parameters and options.

Pros of Stable-Baselines

• More comprehensive library with a wider range of implemented algorithms
• Better documentation and tutorials for beginners
• Integrated with OpenAI Gym environments out of the box

Cons of Stable-Baselines

• Larger codebase, potentially harder to understand and modify
• Less focus on simplicity and readability compared to CleanRL
• May have more dependencies and be harder to set up in some environments

Code Comparison

CleanRL (PPO implementation):

def compute_gae(self, next_value, rewards, masks, values, gamma=0.99, lam=0.95):
    values = values + [next_value]
    gae = 0
    returns = []
    for step in reversed(range(len(rewards))):
        delta = rewards[step] + gamma * values[step + 1] * masks[step] - values[step]
        gae = delta + gamma * lam * masks[step] * gae
        returns.insert(0, gae + values[step])
    return returns

Stable-Baselines (PPO implementation):

def compute_gae(self, rewards, values, dones, last_values, last_dones):
    advantages = np.zeros_like(rewards)
    last_gae_lam = 0
    for step in reversed(range(self.n_steps)):
        if step == self.n_steps - 1:
            next_non_terminal = 1.0 - last_dones
            next_values = last_values
        else:
            next_non_terminal = 1.0 - dones[step + 1]
            next_values = values[step + 1]
        delta = rewards[step] + self.gamma * next_values * next_non_terminal - values[step]
        advantages[step] = last_gae_lam = delta + self.gamma * self.lam * next_non_terminal * last_gae_lam
    return advantages + values

Pros of stable-baselines3

• More comprehensive documentation and tutorials
• Wider range of implemented algorithms
• Better integration with OpenAI Gym environments

Cons of stable-baselines3

• More complex codebase, potentially harder to understand and modify
• Slower training speed due to additional features and abstractions

Code Comparison

stable-baselines3:

from stable_baselines3 import PPO

model = PPO("MlpPolicy", "CartPole-v1", verbose=1)
model.learn(total_timesteps=10000)

CleanRL:

import gym
from cleanrl_utils import parse_args, make_env
from ppo import PPO

args = parse_args()
env = make_env(args.env_id)
agent = PPO(env)
agent.learn(total_timesteps=10000)

Both libraries aim to provide implementations of reinforcement learning algorithms, but they differ in their approach. stable-baselines3 offers a more feature-rich and abstracted interface, while CleanRL focuses on simplicity and readability. The code comparison shows that stable-baselines3 requires less setup but may be less flexible, while CleanRL provides more control over the implementation details at the cost of additional setup code.

Pros of Gymnasium

• Broader scope and more comprehensive environment suite
• Better maintained and more actively developed
• Stronger community support and wider adoption in the RL field

Cons of Gymnasium

• More complex codebase, potentially harder for beginners
• Heavier dependencies and larger installation footprint
• Less focused on specific RL algorithms compared to CleanRL

Code Comparison

Gymnasium example:

import gymnasium as gym
env = gym.make("CartPole-v1")
observation, info = env.reset(seed=42)
for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

CleanRL example:

import gym
env = gym.make("CartPole-v1")
obs = env.reset()
for _ in range(1000):
    action = env.action_space.sample()
    obs, reward, done, info = env.step(action)

The main difference is in the API, with Gymnasium using a more detailed step return and separate reset return values.

Pros of pytorch-a2c-ppo-acktr-gail

• Implements a wider range of algorithms (A2C, PPO, ACKTR, GAIL)
• More established project with a larger community and longer history
• Provides pre-trained models for some environments

Cons of pytorch-a2c-ppo-acktr-gail

• Less focus on code readability and simplicity
• Fewer comments and explanations in the codebase
• Less frequent updates and maintenance

Code Comparison

pytorch-a2c-ppo-acktr-gail:

class Policy(nn.Module):
    def __init__(self, obs_shape, action_space, base=None, base_kwargs=None):
        super(Policy, self).__init__()
        if base_kwargs is None:
            base_kwargs = {}
        if base is None:
            base = MLPBase
        self.base = base(obs_shape[0], **base_kwargs)

cleanrl:

class Agent(nn.Module):
    def __init__(self, envs):
        super().__init__()
        self.critic = nn.Sequential(
            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 1), std=1.0),
        )

The code comparison shows that cleanrl uses a more straightforward and readable approach to defining the neural network architecture, while pytorch-a2c-ppo-acktr-gail uses a more modular and flexible design.

Pros of SpinningUp

• Comprehensive educational resources and tutorials
• Implements a wider range of RL algorithms
• Backed by OpenAI, ensuring high-quality implementations

Cons of SpinningUp

• Less frequently updated compared to CleanRL
• More complex codebase, potentially harder for beginners to understand
• Focuses on PyTorch, while CleanRL supports multiple frameworks

Code Comparison

SpinningUp (PPO implementation):

def ppo(env_fn, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
        steps_per_epoch=4000, epochs=50, gamma=0.99, clip_ratio=0.2, pi_lr=3e-4,
        vf_lr=1e-3, train_pi_iters=80, train_v_iters=80, lam=0.97, max_ep_len=1000,
        target_kl=0.01, logger_kwargs=dict(), save_freq=10):

CleanRL (PPO implementation):

def train(env_id, num_envs, learning_rate, seed, total_timesteps, torch_deterministic, cuda,
          track, wandb_project_name, wandb_entity, capture_video, save_model):

Both repositories provide implementations of popular RL algorithms, but CleanRL focuses on simplicity and readability, while SpinningUp offers more comprehensive educational content and a wider range of algorithms.