rail-berkeley / softlearning
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
☆1,275Updated last year
Alternatives and similar repositories for softlearning:
Users that are interested in softlearning are comparing it to the libraries listed below
- Soft Actor-Critic☆1,056Updated last year
- PyTorch implementation of soft actor critic☆860Updated 3 years ago
- Collection of reinforcement learning algorithms☆2,619Updated 9 months ago
- PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Lear…☆1,177Updated 4 years ago
- PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT…☆1,192Updated last week
- Author's PyTorch implementation of TD3 for OpenAI gym tasks☆1,818Updated last year
- A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.☆1,160Updated 2 years ago
- Inverse RL algorithms (APP, MaxEnt, GAIL, VAIL)☆741Updated last year
- Repo containing code for multi-agent deep reinforcement learning (MADRL).☆686Updated last year
- PyTorch implementation of Soft Actor-Critic (SAC)☆532Updated 3 years ago
- Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.☆846Updated 3 years ago
- Code for the paper "When to Trust Your Model: Model-Based Policy Optimization"☆487Updated 2 years ago
- A collection of reference environments for offline reinforcement learning☆1,434Updated 4 months ago
- Rainbow: Combining Improvements in Deep Reinforcement Learning☆1,608Updated 3 years ago
- PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".☆1,256Updated 5 years ago
- Simple A3C implementation with pytorch + multiprocessing☆634Updated 2 years ago
- Implementation of Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)☆484Updated 2 years ago
- PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.☆558Updated 7 years ago
- RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code☆673Updated 10 months ago
- Simple and easily configurable grid world environments for reinforcement learning☆2,191Updated last month
- Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019☆721Updated 2 years ago
- Author's PyTorch implementation of BCQ for continuous and discrete actions☆615Updated 3 years ago
- Python Multi-Agent Reinforcement Learning framework☆1,972Updated 2 years ago
- Implementations of selected inverse reinforcement learning algorithms.☆1,005Updated 2 years ago
- Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch☆596Updated 6 years ago
- A toolkit for reproducible reinforcement learning research.☆1,943Updated last year
- SMAC: The StarCraft Multi-Agent Challenge☆1,172Updated last year
- Implementation of Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL☆621Updated 10 months ago
- Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch☆1,059Updated 3 years ago
- Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"☆1,739Updated 11 months ago