Need help with cherry?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

158 Stars 23 Forks Apache License 2.0 410 Commits 5 Opened issues


A PyTorch Library for Reinforcement Learning Research

Services available


Need anything else?

Contributors list

Build Status

Cherry is a reinforcement learning framework for researchers built on top of PyTorch.

Unlike other reinforcement learning implementations, cherry doesn't implement a single monolithic interface to existing algorithms. Instead, it provides you with low-level, common tools to write your own algorithms. Drawing from the UNIX philosophy, each tool strives to be as independent from the rest of the framework as possible. So if you don't like a specific tool, you don’t need to use it.


  • Pythonic and low-level interface à la Pytorch.
  • Support for tabular (!) and function approximation algorithms.
  • Various OpenAI Gym environment wrappers.
  • Helper functions for popular algorithms. (e.g. A2C, DDPG, TRPO, PPO, SAC)
  • Logging, visualization, and debugging tools.
  • Painless and efficient distributed training on CPUs and GPUs.
  • Unit, integration, and regression tested, continuously integrated.

To learn more about the tools and philosophy behind cherry, check out our Getting Started tutorial.


The following snippet showcases some of the tools offered by cherry.

import cherry as ch

Wrap environments

env = gym.make('CartPole-v0') env = ch.envs.Logger(env, interval=1000) env = ch.envs.Torch(env)

policy = PolicyNet() optimizer = optim.Adam(policy.parameters(), lr=1e-2) replay = ch.ExperienceReplay() # Manage transitions

for step in range(1000): state = env.reset() while True: mass = Categorical(policy(state)) action = mass.sample() log_prob = mass.log_prob(action) next_state, reward, done, _ = env.step(action)

    # Build the ExperienceReplay
    replay.append(state, action, reward, next_state, done, log_prob=log_prob)
    if done:
        state = next_state

# Discounting and normalizing rewards
rewards =, replay.reward(), replay.done())
rewards = ch.normalize(rewards)

loss = -th.sum(replay.log_prob() * rewards)

Many more high-quality examples are available in the examples/ folder.


Note Cherry is considered in early alpha release. Stuff might break.

pip install cherry-rl


A human-readable changelog is available in the file.


Documentation and tutorials are available on cherry’s website:


First, thanks for your consideration in contributing to cherry. Here are a couple of guidelines we strive to follow.

  • It's always a good idea to open an issue first, where we can discuss how to best proceed.
  • If you want to contribute a new example using cherry, it would preferably stand in a single file.
  • If you would like to contribute a new feature to the core library, we suggest to first implement an example showcasing your new functionality. Doing so is quite useful:
    • it allows for automatic testing,
    • it ensures that the functionality is correctly implemented,
    • it shows users how to use your functionality, and
    • it gives a concrete example when discussing the best way to merge your implementation.

We don't have forums, but are happy to discuss with you on slack. Make sure to send an email to [email protected] to get an invite.


Cherry draws inspiration from many reinforcement learning implementations, including

Why 'cherry' ?

Because it's the sweetest part of the cake.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.