Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For
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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For
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stable-baselines3.SKILL.md
---name: stable-baselines3
description: Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
license: MIT license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.10+, PyTorch >= 2.3, and stable-baselines3 2.8+. Gymnasium environments; optional extras for TensorBoard and Atari (ale-py).
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# Stable Baselines3
## Overview
Stable Baselines3 (SB3) is a PyTorch-based library providing reliable implementations of reinforcement learning algorithms. This skill provides comprehensive guidance for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows using SB3's unified API.
**Current upstream:** SB3 **2.8.0** (April 2026). Docs: [stable-baselines3.readthedocs.io](https://stable-baselines3.readthedocs.io/en/master/).
## Installation
Tested against **stable-baselines3 2.8.0**. Requires **Python 3.10+** (3.9 dropped in 2.8.0) and **PyTorch >= 2.3**.
```bash
# Basic installation
uv pip install "stable-baselines3>=2.8"
See `scripts/train_rl_agent.py` for a complete training template with best practices.
### 2. Custom Environments
**Requirements:**
Custom environments must inherit from `gymnasium.Env` and implement:
- `__init__()`: Define action_space and observation_space
- `reset(seed, options)`: Return initial observation and info dict
- `step(action)`: Return observation, reward, terminated, truncated, info
- `render()`: Visualization (optional)
- `close()`: Cleanup resources
**Key Constraints:**
- Image observations must be `np.uint8` in range [0, 255]
- Use channel-first format when possible (channels, height, width)
- SB3 normalizes images automatically by dividing by 255
- Set `normalize_images=False` in policy_kwargs if pre-normalized
- SB3 does NOT support `Discrete` or `MultiDiscrete` spaces with `start!=0`
**Validation:**
```python
from stable_baselines3.common.env_checker import check_env
check_env(env, warn=True)
```
See `scripts/custom_env_template.py` for a complete custom environment template and `references/custom_environments.md` for comprehensive guidance.
### 3. Vectorized Environments
**Purpose:**
Vectorized environments run multiple environment instances in parallel, accelerating training and enabling certain wrappers (frame-stacking, normalization).
**Types:**
- **DummyVecEnv**: Sequential execution on current process (for lightweight environments)
- **SubprocVecEnv**: Parallel execution across processes (for compute-heavy environments)
**Quick Setup:**
```python
from stable_baselines3.common.env_util import make_vec_env
When using multiple environments with off-policy algorithms (SAC, TD3, DQN), set `gradient_steps=-1` to perform one gradient update per environment step, balancing wall-clock time and sample efficiency.
**API Differences:**
- `reset()` returns only observations (info available in `vec_env.reset_infos`)
- `step()` returns 4-tuple: `(obs, rewards, dones, infos)` not 5-tuple
- Environments auto-reset after episodes
- Terminal observations available via `infos[env_idx]["terminal_observation"]`
See `references/vectorized_envs.md` for detailed information on wrappers and advanced usage.
### 4. Callbacks for Monitoring and Control
**Purpose:**
Callbacks enable monitoring metrics, saving checkpoints, implementing early stopping, and custom training logic without modifying core algorithms.
**Common Callbacks:**
- **EvalCallback**: Evaluate periodically and save best model
- **CheckpointCallback**: Save model checkpoints at intervals
- **StopTrainingOnRewardThreshold**: Stop when target reward reached
- **ProgressBarCallback**: Display training progress with timing
**Custom Callback Structure:**
```python
from stable_baselines3.common.callbacks import BaseCallback
class CustomCallback(BaseCallback):
def _on_training_start(self):
# Called before first rollout
pass
def _on_step(self):
# Called after each environment step
# Return False to stop training
return True
def _on_rollout_end(self):
# Called at end of rollout
pass
```
**Available Attributes:**
- `self.model`: The RL algorithm instance
- `self.num_timesteps`: Total environment steps
- `self.training_env`: The training environment
**Chaining Callbacks:**
```python
from stable_baselines3.common.callbacks import CallbackList
