Added CrossQ

.github/workflows/ci.yml

@@ -33,19 +33,21 @@ jobs:
         run: |
           python -m pip install --upgrade pip
 
+          # Use uv for faster downloads
+          pip install uv
           # Install Atari Roms
-          pip install autorom
+          uv pip install --system autorom
           wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
           base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
           AutoROM --accept-license --source-file Roms.tar.gz
 
-          # cpu version of pytorch - faster to download
-          pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cpu
-
-          pip install -r requirements.txt
+          # See https://github.com/astral-sh/uv/issues/1497
+          uv pip install --system torch==2.4.1+cpu --index https://download.pytorch.org/whl/cpu
+          # Install full requirements (for additional envs and test tools)
+          uv pip install --system -r requirements.txt
           # Use headless version
-          pip install opencv-python-headless
-          pip install -e .[plots,tests]
+          uv pip install --system opencv-python-headless
+          uv pip install --system -e .[plots,tests]
       - name: Lint with ruff
         run: |
           make lint
@@ -62,4 +64,4 @@ jobs:
         if: matrix.python-version != '3.8'
       - name: Test with pytest
         run: |
-          make pytest
+          make pytest

.github/workflows/trained_agents.yml

@@ -34,18 +34,21 @@ jobs:
         run: |
           python -m pip install --upgrade pip
 
+          # Use uv for faster downloads
+          pip install uv
           # Install Atari Roms
-          pip install autorom
+          uv pip install --system autorom
           wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
           base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
           AutoROM --accept-license --source-file Roms.tar.gz
 
-          # cpu version of pytorch - faster to download
-          pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cpu
-          pip install -r requirements.txt
+          # See https://github.com/astral-sh/uv/issues/1497
+          uv pip install --system torch==2.4.1+cpu --index https://download.pytorch.org/whl/cpu
+          # Install full requirements (for additional envs and test tools)
+          uv pip install --system -r requirements.txt
           # Use headless version
-          pip install opencv-python-headless
-          pip install -e .[plots,tests]
+          uv pip install --system opencv-python-headless
+          uv pip install --system -e .[plots,tests]
       - name: Check trained agents
         run: |
           make check-trained-agents

CHANGELOG.md

@@ -1,18 +1,22 @@
-## Release 2.4.0a4 (WIP)
+## Release 2.4.0a10 (WIP)
+
+**New algorithm: CrossQ, and better defaults for SAC/TQC on Swimmer-v4 env**
 
 ### Breaking Changes
 - Updated defaults hyperparameters for TQC/SAC for Swimmer-v4 (decrease gamma for more consistent results) (@JacobHA) [W&B report](https://wandb.ai/openrlbenchmark/sbx/reports/SAC-MuJoCo-Swimmer-v4--Vmlldzo3NzM5OTk2)
 - Upgraded to SB3 >= 2.4.0
 
 ### New Features
+- Added `CrossQ` hyperparameters for SB3-contrib (@danielpalen)
 
 ### Bug fixes
 - Replaced deprecated `huggingface_hub.Repository` when pushing to Hugging Face Hub by the recommended `HfApi` (see https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http) (@cochaviz)
 
 ### Documentation
 
 ### Other
-- Updated PyTorch version to 2.3.1 in the CI
+- Updated PyTorch version to 2.4.1 in the CI
+- Switched to uv to download packages faster on GitHub CI
 
 ## Release 2.3.0 (2024-03-31)
 

hyperparams/crossq.yml

@@ -0,0 +1,91 @@
+MountainCarContinuous-v0:
+  n_timesteps: !!float 50000
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7e-4
+  buffer_size: 50000
+  train_freq: 32
+  gradient_steps: 32
+  gamma: 0.9999
+  learning_starts: 100
+  use_sde: True
+  policy_delay: 2
+  policy_kwargs: "dict(use_expln=True, log_std_init=-1, net_arch=[64, 64])"
+
+Pendulum-v1:
+  n_timesteps: 20000
+  policy: 'MlpPolicy'
+  policy_delay: 2
+  policy_kwargs: "dict(net_arch=[256, 256])"
+
+
+LunarLanderContinuous-v2:
+  n_timesteps: !!float 2e5
+  policy: 'MlpPolicy'
+  buffer_size: 1000000
+  learning_starts: 10000
+
+
+BipedalWalker-v3:
+  n_timesteps: !!float 2e5
+  policy: 'MlpPolicy'
+  buffer_size: 300000
+  gamma: 0.98
+  learning_starts: 10000
+  policy_kwargs: "dict(net_arch=dict(pi=[256, 256], qf=[1024, 1024]))"
+
+# === Mujoco Envs ===
+
+HalfCheetah-v4: &mujoco-defaults
+  buffer_size: 1_000_000
+  learning_rate: !!float 1e-3
+  learning_starts: 5000
+  n_timesteps: !!float 5e6
+  policy: 'MlpPolicy'
+  policy_delay: 3
+  policy_kwargs: "dict(net_arch=dict(pi=[256, 256], qf=[2048, 2048]))"
+
+Ant-v4:
+  <<: *mujoco-defaults
+
+Hopper-v4:
+  <<: *mujoco-defaults
+
+Walker2d-v4:
+  <<: *mujoco-defaults
+
+Humanoid-v4:
+  <<: *mujoco-defaults
+
+HumanoidStandup-v4:
+  <<: *mujoco-defaults
+
+Swimmer-v4:
+  <<: *mujoco-defaults
+  gamma: 0.999
+
+# Tuned for SAC, need to check with CrossQ
+HalfCheetahBulletEnv-v0: &pybullet-defaults
+  n_timesteps: !!float 1e6
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  train_freq: 8
+  gradient_steps: 8
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(use_expln=True, log_std_init=-3)"
+
+# Tuned
+AntBulletEnv-v0:
+  <<: *pybullet-defaults
+
+HopperBulletEnv-v0:
+  <<: *pybullet-defaults
+  learning_rate: lin_7.3e-4
+
+Walker2DBulletEnv-v0:
+  <<: *pybullet-defaults
+  learning_rate: lin_7.3e-4

requirements.txt

@@ -1,5 +1,5 @@
 gym==0.26.2
-stable-baselines3[extra_no_roms,tests,docs]>=2.4.0a0,<3.0
+stable-baselines3[extra_no_roms,tests,docs]>=2.4.0a10,<3.0
 box2d-py==2.3.8
 pybullet_envs_gymnasium>=0.4.0
 # minigrid

rl_zoo3/push_to_hub.py

@@ -10,7 +10,7 @@
 
 import torch as th
 import yaml
-from huggingface_hub import HfApi, Repository
+from huggingface_hub import HfApi
 from huggingface_hub.repocard import metadata_save
 from huggingface_sb3 import EnvironmentName, ModelName, ModelRepoId
 from huggingface_sb3.push_to_hub import _evaluate_agent, _generate_replay, generate_metadata
@@ -83,6 +83,7 @@ def generate_model_card(
 RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
 SB3: https://github.com/DLR-RM/stable-baselines3<br/>
 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+SBX (SB3 + Jax): https://github.com/araffin/sbx
 
 Install the RL Zoo (with SB3 and SB3-Contrib):
 ```bash

rl_zoo3/utils.py

@@ -12,7 +12,7 @@
 from gymnasium import spaces
 from huggingface_hub import HfApi
 from huggingface_sb3 import EnvironmentName, ModelName
-from sb3_contrib import ARS, QRDQN, TQC, TRPO, RecurrentPPO
+from sb3_contrib import ARS, QRDQN, TQC, TRPO, CrossQ, RecurrentPPO
 from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
 from stable_baselines3.common.base_class import BaseAlgorithm
 from stable_baselines3.common.callbacks import BaseCallback
@@ -32,6 +32,7 @@
     "td3": TD3,
     # SB3 Contrib,
     "ars": ARS,
+    "crossq": CrossQ,
     "qrdqn": QRDQN,
     "tqc": TQC,
     "trpo": TRPO,

rl_zoo3/version.txt

@@ -1 +1 @@
-2.4.0a4
+2.4.0a10

setup.py

@@ -15,7 +15,7 @@
 See https://github.com/DLR-RM/rl-baselines3-zoo
 """
 install_requires = [
-    "sb3_contrib>=2.4.0a4,<3.0",
+    "sb3_contrib>=2.4.0a10,<3.0",
     "gymnasium~=0.29.1",
     "huggingface_sb3>=3.0,<4.0",
     "tqdm",
@@ -24,8 +24,7 @@
     "pyyaml>=5.1",
     "pytablewriter~=1.2",
 ]
-# TODO(antonin): update to rliable>=1.1.0 once PR is merged and released
-plots_requires = ["seaborn", "rliable @ git+https://github.com/araffin/rliable@patch-1", "scipy~=1.10"]
+plots_requires = ["seaborn", "rliable~=1.2.0", "scipy~=1.10"]
 test_requires = [
     # for MuJoCo envs v4:
     "mujoco~=2.3",