Pros of Agents

• More comprehensive library with a wider range of algorithms and features
• Better integration with TensorFlow ecosystem and tools
• More active development and community support

Cons of Agents

• Steeper learning curve due to complexity and extensive features
• Potentially slower execution compared to SpinningUp's focused implementation
• Less beginner-friendly documentation and tutorials

Code Comparison

SpinningUp:

import spinup
spinup.ppo(env_fn=lambda : gym.make('CartPole-v1'),
           ac_kwargs=dict(hidden_sizes=[32,32]))

Agents:

from tf_agents.agents.ppo import ppo_agent
agent = ppo_agent.PPOAgent(
    time_step_spec,
    action_spec,
    optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3),
    actor_net=actor_net,
    value_net=value_net)

Summary

Agents offers a more comprehensive toolkit for reinforcement learning with better TensorFlow integration, while SpinningUp provides a simpler, more focused approach for beginners. The choice between them depends on the user's experience level and specific project requirements.

Pros of Stable-baselines

• More comprehensive library with a wider range of algorithms
• Better documentation and more active community support
• Easier to use for practical applications and production environments

Cons of Stable-baselines

• Less focused on educational aspects compared to Spinning Up
• May be more complex for beginners to understand the underlying concepts
• Requires more setup and dependencies

Code Comparison

Spinning Up (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):
    # ... (implementation details)

Stable-baselines (PPO usage):

from stable_baselines3 import PPO

model = PPO("MlpPolicy", "CartPole-v1", verbose=1)
model.learn(total_timesteps=10000)
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)

Pros of stable-baselines3

• More comprehensive and actively maintained library with a wider range of algorithms
• Better documentation and user-friendly API for easier implementation
• Supports both PyTorch and TensorFlow backends

Cons of stable-baselines3

• Steeper learning curve for beginners due to its extensive features
• Less focus on educational aspects compared to SpinningUp's tutorial-style approach

Code Comparison

SpinningUp:

import spinup
spinup.ppo(env_fn=lambda : gym.make('CartPole-v1'),
           ac_kwargs=dict(hidden_sizes=[64,64]), steps_per_epoch=5000,
           epochs=10)

stable-baselines3:

from stable_baselines3 import PPO
model = PPO("MlpPolicy", "CartPole-v1", verbose=1)
model.learn(total_timesteps=50000)

Both repositories provide implementations of reinforcement learning algorithms, but stable-baselines3 offers a more comprehensive and production-ready toolkit, while SpinningUp focuses on educational aspects and simplicity for beginners. The code comparison shows that stable-baselines3 has a more concise API for training models, while SpinningUp provides more explicit configuration options.

Pros of rlkit

• More extensive algorithm implementations, including off-policy and model-based RL
• Greater flexibility and customization options for experiments
• Active development with frequent updates and contributions

Cons of rlkit

• Steeper learning curve due to more complex codebase
• Less comprehensive documentation compared to Spinning Up
• Requires more setup and configuration for running experiments

Code Comparison

rlkit example:

from rlkit.torch.sac.sac import SACTrainer
from rlkit.torch.networks import FlattenMlp
from rlkit.launchers.launcher_util import setup_logger

variant = dict(
    algorithm="SAC",
    version="normal",
    layer_size=256,
    replay_buffer_size=int(1E6),
    algorithm_kwargs=dict(
        num_epochs=3000,
        num_eval_steps_per_epoch=5000,
        num_trains_per_train_loop=1000,
        num_expl_steps_per_train_loop=1000,
        min_num_steps_before_training=1000,
        max_path_length=1000,
        batch_size=256,
    ),
    trainer_kwargs=dict(
        discount=0.99,
        soft_target_tau=5e-3,
        target_update_period=1,
        policy_lr=3E-4,
        qf_lr=3E-4,
        reward_scale=1,
        use_automatic_entropy_tuning=True,
    ),
)

Spinning Up example:

from spinup import sac_pytorch
import gym

env_fn = lambda : gym.make('HalfCheetah-v2')
ac_kwargs = dict(hidden_sizes=[256,256])

sac_pytorch(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=4000, epochs=100,
            replay_size=int(1e6), gamma=0.99, polyak=0.995, lr=1e-3, alpha=0.2,
            batch_size=100, start_steps=10000, update_after=1000, update_every=50,
            num_test_episodes=10, max_ep_len=1000, logger_kwargs=dict(output_dir='sac_pytorch_data'),
            use_cuda=True)

Pros of Dopamine

• More comprehensive library with support for various RL algorithms
• Better integration with TensorFlow and other Google ML tools
• Extensive documentation and examples for different use cases

Cons of Dopamine

• Steeper learning curve for beginners compared to SpinningUp
• Less focus on educational aspects and more on research/production use
• May be overkill for simple RL projects or learning purposes

Code Comparison

SpinningUp (simple 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):
    # ... (implementation details)

Dopamine (DQN agent initialization):

agent = dqn_agent.DQNAgent(
    sess,
    num_actions=environment.action_space.n,
    observation_shape=environment.observation_space.shape,
    observation_dtype=environment.observation_space.dtype,
    stack_size=config.stack_size,
    network=atari_lib.NatureDQNNetwork
)