use brax.v1 and update requirements

.readthedocs.yaml

@@ -8,7 +8,7 @@ version: 2
 build:
  os: ubuntu-20.04
  tools:
- python: "3.8"
+ python: "3.9"
  apt_packages:
  - swig
 

dev.Dockerfile

@@ -1,4 +1,4 @@
-FROM mambaorg/micromamba:0.22.0 as conda
+FROM mambaorg/micromamba:1.5.1 as conda
 
 # Speed up the build, and avoid unnecessary writes to disk
 ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 CONDA_DIR=/opt/conda
@@ -16,7 +16,7 @@ RUN micromamba create -y --file /tmp/environment.yaml \
 
 
 FROM python as test-image
-ENV PATH=/opt/conda/envs/qdaxpy38/bin/:$PATH APP_FOLDER=/app
+ENV PATH=/opt/conda/envs/qdaxpy39/bin/:$PATH APP_FOLDER=/app
 ENV PYTHONPATH=$APP_FOLDER:$PYTHONPATH
 
 COPY --from=conda /opt/conda/envs/. /opt/conda/envs/
@@ -25,8 +25,8 @@ COPY requirements-dev.txt ./
 RUN pip install -r requirements-dev.txt
 
 
-FROM nvidia/cuda:11.4.1-cudnn8-devel-ubuntu20.04 as cuda-image
-ENV PATH=/opt/conda/envs/qdaxpy38/bin/:$PATH APP_FOLDER=/app
+FROM nvidia/cuda:11.5.2-cudnn8-devel-ubuntu20.04 as cuda-image
+ENV PATH=/opt/conda/envs/qdaxpy39/bin/:$PATH APP_FOLDER=/app
 ENV PYTHONPATH=$APP_FOLDER:$PYTHONPATH
 
 
@@ -40,7 +40,7 @@ ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.0/targets/x86_64-linux/l
 
 ENV TZ=Europe/Paris
 RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
-RUN pip --no-cache-dir install jaxlib==0.3.15+cuda11.cudnn82 \
+RUN pip --no-cache-dir install jaxlib==0.4.16+cuda11.cudnn86 \
  -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html \
  && rm -rf /tmp/*
 
@@ -70,7 +70,7 @@ RUN apt-get update && \
  libosmesa6-dev \
  patchelf \
  python3-opengl \
- python3-dev=3.8* \
+ python3-dev=3.9* \
  python3-pip \
  screen \
  sudo \

qdax/__init__.py

@@ -1 +1 @@
-__version__ = "0.2.2"
+__version__ = "0.2.4"

qdax/environments/__init__.py

@@ -1,8 +1,9 @@
 import functools
 from typing import Any, Callable, List, Optional, Union
 
-import brax
-import brax.envs
+from brax.v1.envs import Env
+from brax.v1.envs import _envs
+from brax.v1.envs.wrappers import EpisodeWrapper, AutoResetWrapper, EvalWrapper, VectorWrapper
 
 from qdax.environments.base_wrappers import QDEnv, StateDescriptorResetWrapper
 from qdax.environments.bd_extractors import (
@@ -122,20 +123,20 @@ def create(
  fixed_init_state: bool = False,
  qdax_wrappers_kwargs: Optional[List] = None,
  **kwargs: Any,
-) -> Union[brax.envs.env.Env, QDEnv]:
+) -> Union[Env, QDEnv]:
  """Creates an Env with a specified brax system.
  Please use namespace to avoid confusion between this function and
  brax.envs.create.
  """
 
- if env_name in brax.envs._envs.keys():
- env = brax.envs._envs[env_name](legacy_spring=True, **kwargs)
+ if env_name in _envs.keys():
+ env = _envs[env_name](legacy_spring=True, **kwargs)
  elif env_name in _qdax_envs.keys():
  env = _qdax_envs[env_name](**kwargs)
  elif env_name in _qdax_custom_envs.keys():
  base_env_name = _qdax_custom_envs[env_name]["env"]
- if base_env_name in brax.envs._envs.keys():
- env = brax.envs._envs[base_env_name](legacy_spring=True, **kwargs)
+ if base_env_name in _envs.keys():
+ env = _envs[base_env_name](legacy_spring=True, **kwargs)
  elif base_env_name in _qdax_envs.keys():
  env = _qdax_envs[base_env_name](**kwargs) # type: ignore
  else:
@@ -152,27 +153,27 @@ def create(
  env = wrapper(env, base_env_name, **kwargs) # type: ignore
 
  if episode_length is not None:
- env = brax.envs.wrappers.EpisodeWrapper(env, episode_length, action_repeat)
+ env = EpisodeWrapper(env, episode_length, action_repeat)
  if batch_size:
- env = brax.envs.wrappers.VectorWrapper(env, batch_size)
+ env = VectorWrapper(env, batch_size)
  if fixed_init_state:
  # retrieve the base env
  if env_name not in _qdax_custom_envs.keys():
  base_env_name = env_name
  # wrap the env
  env = FixedInitialStateWrapper(env, base_env_name=base_env_name) # type: ignore
  if auto_reset:
- env = brax.envs.wrappers.AutoResetWrapper(env)
+ env = AutoResetWrapper(env)
  if env_name in _qdax_custom_envs.keys():
  env = StateDescriptorResetWrapper(env)
  if eval_metrics:
- env = brax.envs.wrappers.EvalWrapper(env)
+ env = EvalWrapper(env)
  env = CompletedEvalWrapper(env)
 
  return env
 
 
-def create_fn(env_name: str, **kwargs: Any) -> Callable[..., brax.envs.Env]:
+def create_fn(env_name: str, **kwargs: Any) -> Callable[..., Env]:
  """Returns a function that when called, creates an Env.
  Please use namespace to avoid confusion between this function and
  brax.envs.create_fn.

qdax/environments/base_wrappers.py

@@ -1,8 +1,8 @@
 from abc import abstractmethod
 from typing import Any, List, Tuple
 
-from brax import jumpy as jp
-from brax.envs.env import Env, State
+from brax.v1 import jumpy as jp
+from brax.v1.envs import Env, State
 
 
 class QDEnv(Env):

qdax/environments/exploration_wrappers.py

@@ -1,9 +1,9 @@
 import warnings
 
-import brax
+import brax.v1 as brax
 import jax.numpy as jnp
-from brax import jumpy as jp
-from brax.envs import State, env
+from brax.v1 import jumpy as jp
+from brax.v1.envs import Env, State, Wrapper
 from google.protobuf import text_format # type: ignore
 
 from qdax.environments.locomotion_wrappers import COG_NAMES
@@ -103,7 +103,7 @@
 }
 
 
-class TrapWrapper(env.Wrapper):
+class TrapWrapper(Wrapper):
  """Wraps gym environments to add a Trap in the environment.
 
  Utilisation is simple: create an environment with Brax, pass
@@ -143,7 +143,7 @@ class TrapWrapper(env.Wrapper):
 
  """
 
- def __init__(self, env: env.Env, env_name: str) -> None:
+ def __init__(self, env: Env, env_name: str) -> None:
  if (
  env_name not in ENV_SYSTEM_CONFIG.keys()
  or env_name not in COG_NAMES.keys()
@@ -323,7 +323,7 @@ def step(self, state: State, action: jp.ndarray) -> State:
 }
 
 
-class MazeWrapper(env.Wrapper):
+class MazeWrapper(Wrapper):
  """Wraps gym environments to add a maze in the environment
  and a new reward (distance to the goal).
 
@@ -364,7 +364,7 @@ class MazeWrapper(env.Wrapper):
 
  """
 
- def __init__(self, env: env.Env, env_name: str) -> None:
+ def __init__(self, env: Env, env_name: str) -> None:
  if (
  env_name not in ENV_SYSTEM_CONFIG.keys()
  or env_name not in COG_NAMES.keys()

qdax/environments/humanoidtrap.py

@@ -3,10 +3,10 @@
 
 from typing import Any, Dict
 
-import brax
-from brax import jumpy as jp
-from brax.envs import env
-from brax.physics import bodies
+import brax.v1 as brax
+from brax.v1 import jumpy as jp
+from brax.v1.envs import Env, State
+from brax.v1.physics import bodies
 
 TRAP_CONFIG = """bodies {
  name: "Trap"
@@ -58,7 +58,7 @@
 """
 
 
-class HumanoidTrap(env.Env):
+class HumanoidTrap(Env):
  """Trains a humanoid to run in the +x direction.
 
  RMQ: uses legacy spring from Brax.
@@ -76,7 +76,7 @@ def __init__(self, **kwargs: Dict[str, Any]) -> None:
  self.inertia = body.inertia
  self.inertia_matrix = jp.array([jp.diag(a) for a in self.inertia])
 
- def reset(self, rng: jp.ndarray) -> env.State:
+ def reset(self, rng: jp.ndarray) -> State:
  """Resets the environment to an initial state."""
  rng, rng1, rng2 = jp.random_split(rng, 3)
  qpos = self.sys.default_angle() + jp.random_uniform(
@@ -93,9 +93,9 @@ def reset(self, rng: jp.ndarray) -> env.State:
  "reward_alive": zero,
  "reward_impact": zero,
  }
- return env.State(qp, obs, reward, done, metrics)
+ return State(qp, obs, reward, done, metrics)
 
- def step(self, state: env.State, action: jp.ndarray) -> env.State:
+ def step(self, state: State, action: jp.ndarray) -> State:
  """Run one timestep of the environment's dynamics."""
  qp, info = self.sys.step(state.qp, action)
  obs = self._get_obs(qp, info, action)

qdax/environments/init_state_wrapper.py

@@ -1,8 +1,8 @@
 from typing import Callable, Optional
 
-import brax
-from brax import jumpy as jp
-from brax.envs import Env, State, Wrapper
+import brax.v1 as brax
+from brax.v1 import jumpy as jp
+from brax.v1.envs import Env, State, Wrapper
 
 
 class FixedInitialStateWrapper(Wrapper):
@@ -51,7 +51,7 @@ def reset(self, rng: jp.ndarray) -> State:
  # Run the default reset method of parent environment
  state = self.env.reset(rng)
 
- # Compute new initial positions and velicities
+ # Compute new initial positions and velocities
  qpos = self.sys.default_angle()
  qvel = jp.zeros((self.sys.num_joint_dof,))
 

qdax/environments/locomotion_wrappers.py

@@ -1,11 +1,11 @@
 from typing import Any, List, Optional, Sequence, Tuple
 
 import jax.numpy as jnp
-from brax import jumpy as jp
-from brax.envs import Env, State, Wrapper
-from brax.physics import config_pb2
-from brax.physics.base import QP, Info
-from brax.physics.system import System
+from brax.v1 import jumpy as jp
+from brax.v1.envs import Env, State, Wrapper
+from brax.v1.physics import config_pb2
+from brax.v1.physics.base import QP, Info
+from brax.v1.physics.system import System
 
 from qdax.environments.base_wrappers import QDEnv
 

qdax/environments/pointmaze.py

@@ -1,18 +1,18 @@
 from typing import Any, Dict, List, Tuple, Union
 
-import brax
-from brax import jumpy as jp
-from brax.envs import env
+import brax.v1 as brax
+from brax.v1 import jumpy as jp
+from brax.v1.envs import Env, State
 
 
-class PointMaze(env.Env):
+class PointMaze(Env):
  """Jax/Brax implementation of the PointMaze.
  Highly inspired from the old python implementation of
  the PointMaze.
 
  In order to stay in the Brax API, I will use a fake QP
  at several moment of the implementation. This enable to
- use the brax.envs.env.State from Brax. To avoid this,
+ use the brax.envs.State from Brax. To avoid this,
  it would be good to ask Brax to enlarge a bit their API
  for environments that are not physically simulated.
  """
@@ -103,7 +103,7 @@ def action_size(self) -> int:
  """The size of the observation vector returned in step and reset."""
  return 2
 
- def reset(self, rng: jp.ndarray) -> env.State:
+ def reset(self, rng: jp.ndarray) -> State:
  """Resets the environment to an initial state."""
  rng, rng1, rng2 = jp.random_split(rng, 3)
  # get initial position - reproduce the old implementation
@@ -117,9 +117,9 @@ def reset(self, rng: jp.ndarray) -> env.State:
  metrics: Dict = {}
  # managing state descriptor by our own
  info_init = {"state_descriptor": obs_init}
- return env.State(fake_qp, obs_init, reward, done, metrics, info_init)
+ return State(fake_qp, obs_init, reward, done, metrics, info_init)
 
- def step(self, state: env.State, action: jp.ndarray) -> env.State:
+ def step(self, state: State, action: jp.ndarray) -> State:
  """Run one timestep of the environment's dynamics."""
 
  # clip action taken

qdax/environments/wrappers.py

@@ -1,9 +1,9 @@
 from typing import Dict
 
-import brax.envs
+from brax.v1.envs import State, Wrapper
 import flax.struct
 import jax
-from brax import jumpy as jp
+from brax.v1 import jumpy as jp
 
 
 class CompletedEvalMetrics(flax.struct.PyTreeNode):
@@ -13,12 +13,12 @@ class CompletedEvalMetrics(flax.struct.PyTreeNode):
  completed_episodes_steps: jp.ndarray
 
 
-class CompletedEvalWrapper(brax.envs.env.Wrapper):
+class CompletedEvalWrapper(Wrapper):
  """Brax env with eval metrics for completed episodes."""
 
  STATE_INFO_KEY = "completed_eval_metrics"
 
- def reset(self, rng: jp.ndarray) -> brax.envs.env.State:
+ def reset(self, rng: jp.ndarray) -> State:
  reset_state = self.env.reset(rng)
  reset_state.metrics["reward"] = reset_state.reward
  eval_metrics = CompletedEvalMetrics(
@@ -35,8 +35,8 @@ def reset(self, rng: jp.ndarray) -> brax.envs.env.State:
  return reset_state
 
  def step(
- self, state: brax.envs.env.State, action: jp.ndarray
- ) -> brax.envs.env.State:
+ self, state: State, action: jp.ndarray
+ ) -> State:
  state_metrics = state.info[self.STATE_INFO_KEY]
  if not isinstance(state_metrics, CompletedEvalMetrics):
  raise ValueError(f"Incorrect type for state_metrics: {type(state_metrics)}")

requirements.txt

@@ -1,19 +1,19 @@
 absl-py==1.0.0
-brax==0.0.15
-chex==0.1.5
+brax==0.9.2
+chex==0.1.83
 dm-haiku==0.0.9
-flax==0.6.0
-gym==0.23.1
+flax==0.7.4
+gym==0.26.2
 ipython
-jax==0.3.17
-jaxlib==0.3.15
-jumanji==0.1.3
+jax==0.4.16
+jaxlib==0.4.16
+jumanji==0.3.1
 jupyter
-numpy==1.22.3
-optax==0.1.4
+numpy==1.24.1
+optax==0.1.7
 protobuf==3.19.4
 scikit-learn==1.0.2
 scipy==1.8.0
 seaborn==0.11.2
 tensorflow-probability==0.15.0
-typing-extensions==4.3.0
+typing-extensions==4.3.0