RLPack Basic Edition: Quick Start Guide for BeginnersRLPack Basic Edition is a lightweight, user-friendly toolkit designed to help developers and researchers get started with reinforcement learning (RL) quickly. This guide walks you through the essential concepts, installation, initial configuration, basic workflows, and practical tips to help you move from zero to running your first RL experiments with confidence.
What is RLPack Basic Edition?
RLPack Basic Edition is an entry-level distribution of the RLPack family focused on accessibility and simplicity. It provides core RL components—environments, basic policy implementations, wrappers, logging utilities, and training loops—without the complexity or performance optimizations reserved for the Pro/Enterprise editions. Think of it as a starter toolkit that gets you coding and experimenting fast.
Key facts
- Core components included: simple environments, standard RL algorithms (DQN, PPO-lite), training loop, and logging.
- Target users: beginners, students, and small projects.
- Primary goals: low barrier to entry, easy setup, clear examples.
Who should use the Basic Edition?
RLPack Basic Edition is ideal if you:
- Are new to reinforcement learning and want a gentle learning curve.
- Need a simple way to prototype ideas before scaling up.
- Teach RL concepts in a classroom or workshop setting.
- Want reproducible examples with minimal dependencies.
If you need distributed training, advanced optimization, or large-scale experiment management, consider upgrading to versions that offer those features.
System requirements
RLPack Basic Edition is intentionally lightweight. Typical requirements:
- Python 3.8–3.11
- 4+ GB RAM (8+ GB recommended)
- Optional: NVIDIA GPU with CUDA 11+ for accelerated training (CPU-only workflows are supported)
- pip and virtualenv or conda for environment management
Installation
Use a virtual environment to avoid dependency conflicts. Example with pip and venv:
python3 -m venv rlpack-env source rlpack-env/bin/activate pip install --upgrade pip pip install rlpack-basic If you prefer conda:
conda create -n rlpack python=3.10 conda activate rlpack pip install rlpack-basic Common post-install steps:
- Verify installation with
rlpack --version(or import in Python). - Install optional extras for rendering or GPU support, e.g.,
pip install rlpack-basic[gpu].
Basic concepts and components
Before running experiments, understand a few standard RL concepts and how RLPack models them.
- Environment: The MDP interface providing observations, actions, rewards, and termination signals. RLPack uses a Gym-compatible API.
- Agent/Policy: The decision-maker; maps observations to actions. Basic Edition includes simple neural and tabular policies.
- Replay buffer: Stores experience for off-policy training (used by DQN).
- Training loop: Interacts with the environment, collects data, updates the policy, and logs metrics.
- Metrics and logging: Episode returns, lengths, loss curves, and checkpointing.
First example: Train a DQN on CartPole
Below is a minimal script to train a DQN agent on the classic CartPole environment. Save as train_cartpole.py.
import rlpack from rlpack.envs import make_env from rlpack.agents import DQNAgent from rlpack.utils import Trainer env = make_env("CartPole-v1") agent = DQNAgent(obs_space=env.observation_space, action_space=env.action_space, hidden_sizes=[64, 64], replay_size=10000, batch_size=64) trainer = Trainer(env=env, agent=agent, max_episodes=500, log_interval=10) trainer.train() Run with:
python train_cartpole.py Expected outcomes:
- Training logs printed to console.
- Model checkpoints saved to ./checkpoints by default.
- Average returns increasing over episodes if hyperparameters are reasonable.
Exploring the example: what’s happening
- make_env: wraps Gym env with standard preprocessing.
- DQNAgent: creates a Q-network and an epsilon-greedy policy; handles replay buffer and learning steps.
- Trainer: runs episodes, handles exploration schedule, calls agent.update() regularly, and logs metrics.
Common adjustments and hyperparameters
Tuning is task-dependent, but common knobs:
- learning_rate: start with 1e-3 for small networks.
- batch_size: 32–256.
- replay_size: 10k–1M depending on task.
- gamma (discount): 0.95–0.99.
- exploration schedule: linear epsilon decay from 1.0 to 0.01 over 10k–100k steps.
- target_update_freq (for DQN): 500–2000 steps.
Debugging tips
- If the agent doesn’t learn, confirm environment rewards and observations look reasonable.
- Monitor average episode return and loss — if loss is NaN, reduce learning rate or clip gradients.
- Use a deterministic seed for reproducibility during debugging.
- Start with a smaller network and fewer training steps to iterate faster.
Visualization, logging, and checkpoints
RLPack Basic Edition includes simple logging utilities that save:
- Console logs (training progress).
- CSV/JSON metrics for plotting.
- Model checkpoints (by default under ./checkpoints).
For richer visualization, export logs to TensorBoard:
pip install tensorboard rlpack --logdir ./logs tensorboard Extending Basic Edition
You can extend RLPack Basic Edition by:
- Adding custom Gym environments via the Gym registration API.
- Implementing a new agent by subclassing the Agent base class and overriding select_action() and update() methods.
- Writing custom callbacks for evaluation, early stopping, or custom metrics.
Example skeleton for a custom agent:
from rlpack.agents.base import Agent class MyAgent(Agent): def __init__(self, obs_space, action_space, ...): super().__init__(obs_space, action_space) # build networks, optimizers def select_action(self, obs, eval_mode=False): # return action def update(self, batch): # update networks using batch Example project structure
A recommended lightweight layout:
- rl_project/
- envs/ (custom envs)
- agents/ (custom agents)
- configs/ (yaml/json hyperparameters)
- scripts/
- train.py
- evaluate.py
- logs/
- checkpoints/
Frequently asked questions
Q: Can I use RLPack Basic Edition on GPU? A: Yes, GPU is supported via optional extras; CPU-only workflows are fully functional.
Q: Is Basic suitable for production? A: No — Basic is for learning and prototyping; production workloads need the Pro/Enterprise feature set.
Q: Are environments Gym-compatible? A: Yes — RLPack uses the Gym API for environments.
Next steps
- Run the CartPole example and confirm it improves.
- Try a different environment (MountainCar, LunarLander).
- Implement a simple policy-gradient agent (PPO-lite) from the Basic examples.
- Read source code for agents to learn design patterns.
If you want, I can: provide a ready-to-run starter repo, generate a PPO-lite script, or help tune hyperparameters for a specific environment.
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