Getting SAC to Work on a Massive Parallel Simulator (part II)
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Automatic Hyperparameter Tuning in Practice (blog post)
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Getting SAC to Work on a Massive Parallel Simulator (part I)
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Stable-Baselines3 v2.2 is out!
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Stable-Baselines3 v2.0: Gymnasium Support
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Automatic Hyperparameter Tuning - A Visual Guide
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Stable-Baselines3 v1.8 Release
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Learning to Exploit Elastic Actuators for Quadruped Locomotion
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Stable-Baselines3 v1.1.0: Dictionary observation support, timeout handling and refactored HER buffer
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[P] Stable-Baselines3 v1.0 - Reliable implementations of RL algorithms
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Distributional RL in Stable-Baselines3 contrib (QR-DQN, TQC)
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[R] Generalized State-Dependent Exploration for Deep Reinforcement Learning in Robotics
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[P] Stable-Baselines3 beta, PyTorch edition of the RL Baselines is out!
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
Stable-Baselines Reinforcement Learning Tutorial
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[P] Stable Baselines 2.7.0: Twin Delayed DDPG (TD3)
Beginners -> /r/mlquestions or /r/learnmachinelearning , AGI -> /r/singularity, career advices -> /r/cscareerquestions, datasets -> r/datasets
[N] Hindsight Experience Replay (HER) with SAC/DDPG/DQN support + Evolution Strategy bridge | Stable Baselines v2.6.0
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[N] Hindsight Experience Replay (HER) with SAC/DDPG/DQN support + Evolution Strategy bridge | Stable Baselines v2.6.0
Beginners -> /r/mlquestions or /r/learnmachinelearning , AGI -> /r/singularity, career advices -> /r/cscareerquestions, datasets -> r/datasets
[N] Pre-train your RL agent with Behavior Cloning - Stable-Baselines v2.5.0 Released
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[N] Pre-train your RL agent with Behavior Cloning - Stable-Baselines v2.5.0 Released
Beginners -> /r/mlquestions or /r/learnmachinelearning , AGI -> /r/singularity, career advices -> /r/cscareerquestions, datasets -> r/datasets
[D] What libraries/frameworks do you use for casual reinforcement learning?
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[P] "Learning to Drive Smoothly in Minutes" - Reinforcement Learning on a Small Racing Car (SAC and VAE features)
Beginners -> /r/mlquestions or /r/learnmachinelearning , AGI -> /r/singularity, career advices -> /r/cscareerquestions, datasets -> r/datasets
[N] Stable-Baselines 2.4.0 released: Soft Actor-Critic (SAC) and easy policy customization
Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.
[N] Stable-Baselines 2.4.0 released: Soft Actor-Critic (SAC) and easy policy customization
Beginners -> /r/mlquestions or /r/learnmachinelearning , AGI -> /r/singularity, career advices -> /r/cscareerquestions, datasets -> r/datasets
