Pros of Gym

• Widely adopted and supported by the RL community
• Extensive collection of pre-built environments
• Simple and intuitive API for environment interaction

Cons of Gym

• Limited built-in support for advanced RL algorithms
• Lacks integrated visualization tools for training progress
• Fewer options for customizing environment parameters

Code Comparison

Gym:

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

Garage:

from garage import wrap_experiment
from garage.envs import GymEnv
from garage.experiment import LocalRunner
from garage.sampler import RaySampler
from garage.tf.algos import PPO
from garage.tf.policies import GaussianMLPPolicy

@wrap_experiment
def ppo_cartpole(ctxt=None):
    env = GymEnv('CartPole-v1')
    policy = GaussianMLPPolicy(env.spec)
    sampler = RaySampler(agents=policy, envs=env)
    algo = PPO(env_spec=env.spec, policy=policy, sampler=sampler)
    runner = LocalRunner(ctxt)
    runner.setup(algo, env)
    runner.train(n_epochs=100, batch_size=4000)

Pros of Stable-baselines

• More user-friendly and easier to get started with
• Better documentation and examples
• Wider range of implemented algorithms

Cons of Stable-baselines

• Less flexible for custom environments and modifications
• Fewer options for advanced users and researchers

Code Comparison

Stable-baselines:

from stable_baselines3 import PPO
model = PPO("MlpPolicy", "CartPole-v1", verbose=1)
model.learn(total_timesteps=10000)

Garage:

from garage import wrap_experiment
from garage.tf.algos import PPO
from garage.tf.policies import GaussianMLPPolicy

@wrap_experiment
def ppo_cartpole(ctxt=None):
    policy = GaussianMLPPolicy(env_spec=env.spec)
    algo = PPO(env_spec=env.spec, policy=policy)
    trainer.setup(algo, env)
    trainer.train(n_epochs=100, batch_size=4000)

Pros of TensorFlow Agents

• Built on TensorFlow, offering seamless integration with TensorFlow ecosystem
• Extensive documentation and tutorials for easier onboarding
• Regular updates and active maintenance by Google's team

Cons of TensorFlow Agents

• Limited to TensorFlow backend, less flexible than Garage's multi-framework support
• Steeper learning curve for those not familiar with TensorFlow
• Fewer algorithm implementations compared to Garage's diverse collection

Code Comparison

Garage example:

from garage import wrap_experiment
from garage.tf.algos import PPO
from garage.tf.baselines import GaussianMLPBaseline
from garage.tf.envs import TfEnv
from garage.tf.experiment import LocalTFRunner
from garage.tf.policies import GaussianMLPPolicy

@wrap_experiment
def ppo_garage(ctxt=None):
    with LocalTFRunner(ctxt) as runner:
        env = TfEnv(normalize(gym.make('HalfCheetah-v2')))
        policy = GaussianMLPPolicy(env.spec)
        baseline = GaussianMLPBaseline(env.spec)
        algo = PPO(env_spec=env.spec, policy=policy, baseline=baseline)
        runner.setup(algo, env)
        runner.train(n_epochs=100, batch_size=4000)

TensorFlow Agents example:

import tensorflow as tf
from tf_agents.agents.ppo import ppo_agent
from tf_agents.environments import suite_gym
from tf_agents.networks import actor_distribution_network, value_network

env = suite_gym.load('HalfCheetah-v2')
actor_net = actor_distribution_network.ActorDistributionNetwork(
    env.observation_spec(), env.action_spec())
value_net = value_network.ValueNetwork(env.observation_spec())
agent = ppo_agent.PPOAgent(
    env.time_step_spec(), env.action_spec(), actor_net, value_net)

Pros of stable-baselines3

• More active development and frequent updates
• Better documentation and tutorials for beginners
• Wider range of implemented algorithms

Cons of stable-baselines3

• Less flexibility for customization and experimentation
• Fewer options for parallel training and distributed computing

Code Comparison

stable-baselines3:

from stable_baselines3 import PPO

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

garage:

from garage import wrap_experiment
from garage.tf.algos import PPO
from garage.tf.policies import GaussianMLPPolicy

@wrap_experiment
def ppo_cartpole(ctxt=None):
    policy = GaussianMLPPolicy(env_spec=env.spec)
    algo = PPO(env_spec=env.spec, policy=policy)
    trainer.setup(algo, env)
    trainer.train(n_epochs=100, batch_size=4000)

Both libraries offer implementations of popular RL algorithms, but stable-baselines3 provides a more straightforward API for quick experimentation, while garage offers more flexibility for advanced users and researchers. The code comparison shows that stable-baselines3 requires less boilerplate code to get started, making it more accessible for beginners.

Pros of softlearning

• Focuses on soft actor-critic (SAC) algorithms, providing specialized implementations
• Includes pre-trained models and benchmarks for easy comparison
• Offers integration with RLlib for distributed training

Cons of softlearning

• Less versatile than garage, primarily centered on SAC variants
• Smaller community and fewer contributors compared to garage
• Limited documentation and examples for beginners

Code Comparison

softlearning:

from softlearning.environments.utils import get_environment
from softlearning.algorithms.sac import SAC

env = get_environment('Ant-v2')
algorithm = SAC(env=env, Q_lr=3e-4, policy_lr=3e-4)
algorithm.train()

garage:

from garage import wrap_experiment
from garage.envs import GymEnv
from garage.experiment import LocalTFRunner
from garage.tf.algos import SAC

@wrap_experiment
def sac_ant(ctxt=None):
    with LocalTFRunner(ctxt) as runner:
        env = GymEnv('Ant-v2')
        algo = SAC(env_spec=env.spec, qf_lr=3e-4, policy_lr=3e-4)
        runner.setup(algo, env)
        runner.train(n_epochs=500, batch_size=256)

Pros of Gymnasium

• More active development and community support
• Wider range of pre-built environments for various RL tasks
• Better documentation and tutorials for beginners

Cons of Gymnasium

• Less focus on modular, composable RL components
• Fewer built-in algorithms and policy implementations
• Limited support for distributed training

Code Comparison

Gymnasium:

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)

Garage:

from garage import wrap_experiment
from garage.envs import GymEnv
from garage.experiment import LocalRunner
@wrap_experiment
def my_experiment(ctxt=None):
    env = GymEnv('CartPole-v1')
    runner = LocalRunner(ctxt)
    runner.setup(algo, env)
    runner.train(n_epochs=100, batch_size=4000)

The code comparison shows that Gymnasium focuses on simplicity and ease of use, while Garage provides a more structured approach with experiment management and runner classes. Gymnasium's API is more straightforward for beginners, while Garage offers more flexibility for complex RL setups.