Stable Baselines3 Algorithm Reference

This document provides detailed characteristics of all RL algorithms in Stable Baselines3 to help select the right algorithm for specific tasks.

Algorithm Comparison Table

Detailed Algorithm Characteristics

PPO (Proximal Policy Optimization)

Overview: General-purpose on-policy algorithm with good performance across many tasks.

Strengths:

• Stable and reliable training
• Works with all action space types (Discrete, Box, MultiDiscrete, MultiBinary)
• Good balance between sample efficiency and training speed
• Excellent for multiprocessing with vectorized environments
• Easy to tune

Weaknesses:

• Less sample-efficient than off-policy methods
• Requires many environment interactions

Best For:

• General-purpose RL tasks
• When stability is important
• When you have cheap environment simulations
• Tasks with continuous or discrete actions

Hyperparameter Guidance:

• n_steps: 2048-4096 for continuous, 128-256 for Atari
• learning_rate: 3e-4 is a good default
• n_epochs: 10 for continuous, 4 for Atari
• batch_size: 64
• gamma: 0.99 (0.995-0.999 for long episodes)

A2C (Advantage Actor-Critic)

Overview: Synchronous variant of A3C, simpler than PPO but less stable.

Strengths:

• Very fast training (simpler than PPO)
• Works with all action space types
• Good for quick prototyping
• Memory efficient

Weaknesses:

• Less stable than PPO
• Requires careful hyperparameter tuning
• Lower sample efficiency

Best For:

• Quick experimentation
• When training speed is critical
• Simple environments

Hyperparameter Guidance:

• n_steps: 5-256 depending on task
• learning_rate: 7e-4
• gamma: 0.99

SAC (Soft Actor-Critic)

Overview: Off-policy algorithm with entropy regularization, state-of-the-art for continuous control.

Strengths:

• Excellent sample efficiency
• Very stable training
• Automatic entropy tuning
• Good exploration through stochastic policy
• State-of-the-art for robotics

Weaknesses:

• Only supports continuous action spaces (Box)
• Slower wall-clock time than on-policy methods
• More complex hyperparameters

Best For:

• Continuous control (robotics, physics simulations)
• When sample efficiency is critical
• Expensive environment simulations
• Tasks requiring good exploration

Hyperparameter Guidance:

• learning_rate: 3e-4
• buffer_size: 1M for most tasks
• learning_starts: 10000
• batch_size: 256
• tau: 0.005 (target network update rate)
• train_freq: 1 with gradient_steps=-1 for best performance

TD3 (Twin Delayed DDPG)

Overview: Improved DDPG with double Q-learning and delayed policy updates.

Strengths:

• High sample efficiency
• Deterministic policy (good for deployment)
• More stable than DDPG
• Good for continuous control

Weaknesses:

• Only supports continuous action spaces (Box)
• Less exploration than SAC
• Requires careful tuning

Best For:

• Continuous control tasks
• When deterministic policies are preferred
• Sample-efficient learning

Hyperparameter Guidance:

• learning_rate: 1e-3
• buffer_size: 1M
• learning_starts: 10000
• batch_size: 100
• policy_delay: 2 (update policy every 2 critic updates)

DDPG (Deep Deterministic Policy Gradient)

Overview: Early off-policy continuous control algorithm.

Strengths:

• Continuous action space support
• Off-policy learning

Weaknesses:

• Less stable than TD3 or SAC
• Sensitive to hyperparameters
• Generally outperformed by TD3

Best For:

• Legacy compatibility
• Recommendation: Use TD3 instead for new projects

DQN (Deep Q-Network)

Overview: Classic off-policy algorithm for discrete action spaces.

Strengths:

• Sample-efficient for discrete actions
• Experience replay enables reuse of past data
• Proven success on Atari games

Weaknesses:

• Only supports discrete action spaces
• Can be unstable without proper tuning
• Overestimation bias

Best For:

• Discrete action tasks
• Atari games and similar environments
• When sample efficiency matters

Hyperparameter Guidance:

• learning_rate: 1e-4
• buffer_size: 100K-1M depending on task
• learning_starts: 50000 for Atari
• batch_size: 32
• exploration_fraction: 0.1
• exploration_final_eps: 0.05

Variants:

• QR-DQN: Distributional RL version for better value estimates
• Maskable DQN: For environments with action masking

HER (Hindsight Experience Replay)

Overview: Not a standalone algorithm but a replay buffer strategy for goal-conditioned tasks.

Strengths:

• Dramatically improves learning in sparse reward settings
• Learns from failures by relabeling goals
• Works with any off-policy algorithm (SAC, TD3, DQN)

Weaknesses:

• Only for goal-conditioned environments
• Requires specific observation structure (Dict with "observation", "achieved_goal", "desired_goal")

Best For:

• Goal-conditioned tasks (robotics manipulation, navigation)
• Sparse reward environments
• Tasks where goal is clear but reward is binary

Usage:

from stable_baselines3 import SAC, HerReplayBuffer

model = SAC(
    "MultiInputPolicy",
    env,
    replay_buffer_class=HerReplayBuffer,
    replay_buffer_kwargs=dict(
        n_sampled_goal=4,
        goal_selection_strategy="future",  # or "episode", "final"
    ),
)

RecurrentPPO

Overview: PPO with LSTM policy for handling partial observability.

Strengths:

• Handles partial observability (POMDP)
• Can learn temporal dependencies
• Good for memory-required tasks

Weaknesses:

• Slower training than standard PPO
• More complex to tune
• Requires sequential data

Best For:

• Partially observable environments
• Tasks requiring memory (e.g., navigation without full map)
• Time-series problems

Algorithm Selection Guide

Decision Tree

1. What is your action space?

   • Continuous (Box) → Consider PPO, SAC, or TD3
   • Discrete → Consider PPO, A2C, or DQN
   • MultiDiscrete/MultiBinary → Use PPO or A2C

2. Is sample efficiency critical?

   • Yes (expensive simulations) → Use off-policy: SAC, TD3, DQN, or HER
   • No (cheap simulations) → Use on-policy: PPO, A2C

3. Do you need fast wall-clock training?

   • Yes → Use PPO or A2C with vectorized environments
   • No → Any algorithm works

4. Is the task goal-conditioned with sparse rewards?

   • Yes → Use HER with SAC or TD3
   • No → Continue with standard algorithms

5. Is the environment partially observable?

   • Yes → Use RecurrentPPO
   • No → Use standard algorithms

Quick Recommendations

• Starting out / General tasks: PPO
• Continuous control / Robotics: SAC
• Discrete actions / Atari: DQN or PPO
• Goal-conditioned / Sparse rewards: SAC + HER
• Fast prototyping: A2C
• Sample efficiency critical: SAC, TD3, or DQN
• Partial observability: RecurrentPPO

Training Configuration Tips

For On-Policy Algorithms (PPO, A2C)

# Use vectorized environments for speed
env = make_vec_env(env_id, n_envs=8, vec_env_cls=SubprocVecEnv)

model = PPO(
    "MlpPolicy",
    env,
    n_steps=2048,  # Collect this many steps per environment before update
    batch_size=64,
    n_epochs=10,
    learning_rate=3e-4,
    gamma=0.99,
)

For Off-Policy Algorithms (SAC, TD3, DQN)

# Fewer environments, but use gradient_steps=-1 for efficiency
env = make_vec_env(env_id, n_envs=4)

model = SAC(
    "MlpPolicy",
    env,
    buffer_size=1_000_000,
    learning_starts=10000,
    batch_size=256,
    train_freq=1,
    gradient_steps=-1,  # Do 1 gradient step per env step (4 with 4 envs)
    learning_rate=3e-4,
)

Common Pitfalls

1. Using DQN with continuous actions - DQN only works with discrete actions
2. Not using vectorized environments with PPO/A2C - Wastes potential speedup
3. Using too few environments - On-policy methods need many samples
4. Using too large replay buffer - Can cause memory issues
5. Not tuning learning rate - Critical for stable training
6. Ignoring reward scaling - Normalize rewards for better learning
7. Wrong policy type - Use "CnnPolicy" for images, "MultiInputPolicy" for dict observations

Performance Benchmarks

Approximate expected performance (mean reward) on common benchmarks:

Continuous Control (MuJoCo)

• HalfCheetah-v3: PPO ~1800, SAC ~12000, TD3 ~9500
• Hopper-v3: PPO ~2500, SAC ~3600, TD3 ~3600
• Walker2d-v3: PPO ~3000, SAC ~5500, TD3 ~5000

Discrete Control (Atari)

• Breakout: PPO ~400, DQN ~300
• Pong: PPO ~20, DQN ~20
• Space Invaders: PPO ~1000, DQN ~800

Note: Performance varies significantly with hyperparameters and training time.

Additional Resources

• RL Baselines3 Zoo: Collection of pre-trained agents and hyperparameters: https://github.com/DLR-RM/rl-baselines3-zoo
• Hyperparameter Tuning: Use Optuna for systematic tuning
• Custom Policies: Extend base policies for custom network architectures
• Contribution Repo: SB3-Contrib for experimental algorithms (QR-DQN, TQC, etc.)