Pros of baselines

• More comprehensive set of RL algorithms implemented
• Better documentation and examples
• Active development and maintenance by OpenAI

Cons of baselines

• Steeper learning curve for beginners
• Less focus on integration with Keras

Code Comparison

baselines:

from baselines import deepq
from baselines.common.atari_wrappers import wrap_deepmind

env = wrap_deepmind(gym.make("PongNoFrameskip-v4"))
model = deepq.learn(env, network='cnn', total_timesteps=100000)

keras-rl:

from rl.agents.dqn import DQNAgent
from rl.policy import EpsGreedyQPolicy
from rl.memory import SequentialMemory

model = create_model(input_shape, nb_actions)
memory = SequentialMemory(limit=50000, window_length=1)
policy = EpsGreedyQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, policy=policy)

Both repositories provide implementations of reinforcement learning algorithms, but baselines offers a wider range of algorithms and more extensive documentation. However, keras-rl may be more suitable for those already familiar with Keras and looking for a simpler integration. The code examples demonstrate the different approaches to implementing a DQN agent in each library.

Pros of stable-baselines

• More comprehensive and actively maintained library
• Supports a wider range of algorithms and environments
• Better documentation and community support

Cons of stable-baselines

• Steeper learning curve for beginners
• Requires more computational resources for some algorithms

Code Comparison

stable-baselines:

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv

env = DummyVecEnv([lambda: gym.make("CartPole-v1")])
model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=10000)

keras-rl:

from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory

model = Sequential()
model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))

memory = SequentialMemory(limit=50000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
               target_model_update=1e-2, policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=50000, visualize=False, verbose=2)

The code comparison shows that stable-baselines offers a more concise and straightforward implementation, while keras-rl requires more manual setup and configuration.

Pros of TF-Agents

• More comprehensive and actively maintained
• Better integration with TensorFlow ecosystem
• Supports both TensorFlow and TensorFlow Probability

Cons of TF-Agents

• Steeper learning curve for beginners
• More complex setup and configuration
• Potentially slower development for simple projects

Code Comparison

keras-rl:

from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory

dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
               target_model_update=1e-2, policy=BoltzmannQPolicy())
dqn.compile(Adam(lr=1e-3), metrics=['mae'])

TF-Agents:

from tf_agents.agents.dqn import dqn_agent
from tf_agents.networks import q_network
from tf_agents.replay_buffers import tf_uniform_replay_buffer

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

Pros of pfrl

• More comprehensive and up-to-date implementation of reinforcement learning algorithms
• Better support for distributed training and parallel environments
• More extensive documentation and examples

Cons of pfrl

• Steeper learning curve due to its more complex architecture
• Less integration with Keras, which may be a drawback for Keras users
• Requires more setup and configuration compared to keras-rl

Code Comparison

keras-rl example:

from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory

model = create_model()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model=model, memory=memory, policy=BoltzmannQPolicy())
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)

pfrl example:

import pfrl

q_func = create_q_function()
optimizer = torch.optim.Adam(q_func.parameters(), lr=1e-3)
replay_buffer = pfrl.replay_buffers.ReplayBuffer(capacity=10**6)
explorer = pfrl.explorers.LinearDecayEpsilonGreedy(start_epsilon=1.0, end_epsilon=0.1, decay_steps=50000)
agent = pfrl.agents.DoubleDQN(q_func, optimizer, replay_buffer, gamma=0.99, explorer=explorer)
pfrl.experiments.train_agent_with_evaluation(agent, env, steps=50000, eval_n_steps=None, eval_n_episodes=10)

Pros of garage

• More comprehensive and flexible framework for RL research
• Supports a wider range of algorithms and environments
• Better documentation and examples for advanced users

Cons of garage

• Steeper learning curve for beginners
• Less focus on simplicity and ease of use
• Requires more setup and configuration

Code Comparison

keras-rl:

from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory

dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
               target_model_update=1e-2, policy=BoltzmannQPolicy())
dqn.compile(Adam(lr=1e-3), metrics=['mae'])

garage:

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

@wrap_experiment
def trpo_cartpole(ctxt=None):
    policy = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
    algo = TRPO(env=env, policy=policy, baseline=baseline, max_path_length=100)