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Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
| Date | Stars |
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| 2026-07-24 | 1434 |
| 2026-07-25 | 1434 |
| 2026-07-28 | 1434 |
| 2026-07-30 | 1434 |
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| 2026-08-04 | 1435 |
| 2026-08-06 | 1435 |
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# Softlearning
Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is fairly thin and primarily optimized for our own development purposes. It utilizes the tf.keras modules for most of the model classes (e.g. policies and value functions). We use Ray for the experiment orchestration. Ray Tune and Autoscaler implement several neat features that enable us to seamlessly run the same experiment scripts that we use for local prototyping to launch large-scale experiments on any chosen cloud service (e.g. GCP or AWS), and intelligently parallelize and distribute training for effective resource allocation.
This implementation uses Tensorflow. For a PyTorch implementation of soft actor-critic, take a look at [rlkit](https://github.com/vitchyr/rlkit).
# Getting Started
## Prerequisites
The environment can be run either locally using conda or inside a docker container. For conda installation, you need to have [Conda](https://conda.io/docs/user-guide/install/index.html) installed. For docker installation you will need to have [Docker](https://docs.docker.com/engine/installation/) and [Docker Compose](https://docs.docker.com/compose/install/) installed. Also, most of our environments currently require a [MuJoCo](https://www.roboti.us/license.html) license.
## Conda Installation
1. [Download](https://www.roboti.us/index.html) and install MuJoCo 1.50 and 2.00 from the MuJoCo website. We assume that the MuJoCo files are extracted to the default location (`~/.mujoco/mjpro150` and `~/.mujoco/mujoco200_{platform}`). Unfortunately, `gym` and `dm_control` expect different paths for MuJoCo 2.00 installation, which is why you will need to have it installed both in `~/.mujoco/mujoco200_{platform}` and `~/.mujoco/mujoco200`. The easiest way is to create a symlink from `~/.mujoco/mujoco200_{plaftorm}` -> `~/.mujoco/mujoco200` with: `ln -s ~/.mujoco/mujoco200_{platform} ~/.mujoco/mujoco200`.
2. Copy your MuJoCo license key (mjkey.txt) to ~/.mujoco/mjkey.txt:
3. Clone `softlearning`
```
git clone https://github.com/rail-berkeley/softlearning.git ${SOFTLEARNING_PATH}
```
4. Create and activate conda environment, install softlearning to enable command line interface.
```
cd ${SOFTLEARNING_PATH}
conda env create -f environment.yml
conda activate softlearning
pip install -e ${SOFTLEARNING_PATH}
```
The environment should be ready to run. See examples section for examples of how to train and simulate the agents.
Finally, to deactivate and remove the conda environment:
```
conda deactivate
conda remove --name softlearning --all
```
## Docker Installation
### docker-compose
To build the image and run the container:
```
export MJKEY="$(cat ~/.mujoco/mjkey.txt)" \
&& docker-compose \
-f ./docker/docker-compose.dev.cpu.yml \
up \
-d \
--force-recreate
```
You can access the container with the typical Docker [exec](https://docs.docker.com/engine/reference/commandline/exec/)-command, i.e.
```
docker exec -it softlearning bash
```
See examples section for examples of how to train and simulate the agents.
Finally, to clean up the docker setup:
```
docker-compose \
-f ./docker/docker-compose.dev.cpu.yml \
down \
--rmi all \
--volumes
```
## Examples
### Training and simulating an agent
1. To train the agent
```
softlearning run_example_local examples.development \
--algorithm SAC \
--universe gym \
--domain HalfCheetah \
--task v3 \
--exp-name my-sac-experiment-1 \
--checkpoint-frequency 1000 # Save the checkpoint to resume training later
```
2. To simulate the resulting policy:
First, find the *absolute* path that the checkpoint is saved to. By default (i.e. without specifying the `log-dir` argument to the previous script), the data is saved under `~/ray_results/<universe>/<domain>/<task>/<datatimestamp>-<exp-name>/<trial-id>/<checkpoint-id>`. For example: `~/ray_results/gym/HalfCheetah/v3/20Excerpt of 11,882 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f5e05f453350e14d, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning