Pros of Dopamine

• Simpler and more focused on specific RL algorithms (DQN, Rainbow, C51)
• Easier to get started with for beginners in reinforcement learning
• Designed for reproducibility and easy experimentation

Cons of Dopamine

• Less comprehensive than Agents, with fewer algorithms and features
• Limited to Atari environments, while Agents supports a wider range of tasks
• Less active development and community support compared to Agents

Code Comparison

Dopamine (training a DQN agent):

runner = Runner(base_dir, create_agent_fn)
runner.run_experiment()

Agents (training a DQN agent):

agent = dqn_agent.DqnAgent(
    time_step_spec,
    action_spec,
    q_network=q_net,
    optimizer=optimizer)
collect_driver.run()

Both libraries offer concise ways to train RL agents, but Dopamine's API is slightly more abstracted and easier to use out of the box. Agents provides more flexibility and control over the training process, which can be beneficial for advanced users and researchers.

Pros of Baselines

• Wider range of implemented algorithms, including PPO, TRPO, and DDPG
• More extensive documentation and examples for various environments
• Easier to use for beginners due to simpler API and setup process

Cons of Baselines

• Less active development and maintenance compared to Agents
• Limited integration with TensorFlow ecosystem
• Fewer options for customization and extension of algorithms

Code Comparison

Baselines (PPO implementation):

from baselines.ppo2 import ppo2
from baselines.common.vec_env import DummyVecEnv

env = DummyVecEnv([lambda: gym.make("CartPole-v1")])
model = ppo2.learn(env=env, total_timesteps=10000)

Agents (PPO implementation):

from tf_agents.agents.ppo import ppo_agent
from tf_agents.environments import suite_gym

env = suite_gym.load("CartPole-v1")
agent = ppo_agent.PPOAgent(
    time_step_spec=env.time_step_spec(),
    action_spec=env.action_spec(),
    optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3),
)

Both repositories offer robust implementations of reinforcement learning algorithms, but they cater to different user needs. Baselines provides a more accessible entry point for beginners and a wider range of algorithms, while Agents offers deeper integration with TensorFlow and more flexibility for advanced users.

Pros of Stable-baselines

• Easier to use and more beginner-friendly
• Provides a unified interface for various RL algorithms
• Well-documented with extensive examples and tutorials

Cons of Stable-baselines

• Less flexible for advanced customization
• Fewer cutting-edge algorithms compared to TF-Agents
• Limited support for distributed training

Code Comparison

Stable-baselines:

from stable_baselines3 import PPO

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

TF-Agents:

from tf_agents.agents.ppo import ppo_agent
from tf_agents.networks import actor_distribution_network, value_network

actor_net = actor_distribution_network.ActorDistributionNetwork(
    obs_spec, action_spec, fc_layer_params=(200, 100))
value_net = value_network.ValueNetwork(obs_spec, fc_layer_params=(200, 100))

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

Both libraries offer implementations of popular RL algorithms, but TF-Agents provides more granular control over agent components and training processes. Stable-baselines focuses on simplicity and ease of use, making it more accessible for beginners and rapid prototyping. TF-Agents, being part of the TensorFlow ecosystem, offers better integration with other TensorFlow tools and more advanced features for researchers and experienced practitioners.

Pros of Acme

• More flexible and modular architecture, allowing easier customization of agents and environments
• Supports both TensorFlow and JAX, offering greater flexibility in backend choice
• Provides a wider range of pre-implemented algorithms, including more recent advancements in RL

Cons of Acme

• Steeper learning curve due to its more complex architecture
• Less integrated with TensorFlow ecosystem compared to Agents
• Documentation may be less comprehensive for beginners

Code Comparison

Agents example:

agent = dqn_agent.DqnAgent(
    time_step_spec,
    action_spec,
    q_network=q_net,
    optimizer=optimizer,
    td_errors_loss_fn=common.element_wise_squared_loss,
    train_step_counter=train_step_counter)

Acme example:

agent = dqn.DQN(
    environment_spec=environment_spec,
    network=network,
    batch_size=batch_size,
    samples_per_insert=samples_per_insert,
    min_replay_size=min_replay_size)

Both repositories offer robust implementations of reinforcement learning algorithms, but Acme provides more flexibility and a wider range of algorithms, while Agents offers tighter integration with the TensorFlow ecosystem and potentially easier onboarding for beginners.

Pros of PFRL

• Built on PyTorch, offering dynamic computation graphs and easier debugging
• Extensive collection of implemented algorithms, including advanced options like SAC and TD3
• Flexible and modular design, allowing easy customization of components

Cons of PFRL

• Smaller community and less extensive documentation compared to TensorFlow Agents
• Fewer integrations with external tools and environments
• May have a steeper learning curve for those familiar with TensorFlow ecosystem

Code Comparison

PFRL (PyTorch):

import torch
import pfrl

q_func = torch.nn.Sequential(
    torch.nn.Linear(obs_size, 64),
    torch.nn.ReLU(),
    torch.nn.Linear(64, n_actions)
)
optimizer = torch.optim.Adam(q_func.parameters())
explorer = pfrl.explorers.ConstantEpsilonGreedy(epsilon=0.3, random_action_func=env.action_space.sample)

TensorFlow Agents:

import tensorflow as tf
from tf_agents.networks import q_network
from tf_agents.agents.dqn import dqn_agent

q_net = q_network.QNetwork(
    input_tensor_spec,
    action_spec,
    fc_layer_params=(64,)
)
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3)
agent = dqn_agent.DqnAgent(
    time_step_spec,
    action_spec,
    q_network=q_net,
    optimizer=optimizer
)

Pros of garage

• More flexible and modular architecture, allowing easier customization of algorithms
• Supports multiple deep learning frameworks (TensorFlow, PyTorch, and Theano)
• Extensive documentation and tutorials for beginners

Cons of garage

• Smaller community and less frequent updates compared to agents
• Fewer pre-implemented algorithms and environments
• May require more setup and configuration for complex tasks

Code Comparison

garage example:

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

@wrap_experiment
def ppo_experiment(ctxt=None):
    policy = GaussianMLPPolicy(env_spec=env.spec)
    algo = PPO(env_spec=env.spec, policy=policy)
    trainer.setup(algo, env)
    trainer.train()

agents example:

import tensorflow as tf
from tf_agents.agents.ppo import ppo_agent
from tf_agents.networks import actor_distribution_network

actor_net = actor_distribution_network.ActorDistributionNetwork(
    input_tensor_spec, output_tensor_spec)
agent = ppo_agent.PPOAgent(
    time_step_spec, action_spec, actor_net=actor_net)

Both repositories offer reinforcement learning frameworks, but garage provides more flexibility and support for multiple deep learning libraries, while agents is more tightly integrated with TensorFlow and offers a larger selection of pre-implemented algorithms.