updated examples to new api,

fancy_gym/examples/examples_dmc.py

@@ -26,10 +26,10 @@ def example_dmc(env_id="dmc:fish-swim", seed=1, iterations=1000, render=True):
         ac = env.action_space.sample()
         if render:
             env.render(mode="human")
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
 
-        if done:
+        if terminated or truncated:
             print(env_id, rewards)
             rewards = 0
             obs = env.reset()
@@ -102,10 +102,10 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
     # number of samples/full trajectories (multiple environment steps)
     for i in range(iterations):
         ac = env.action_space.sample()
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
 
-        if done:
+        if terminated or truncated:
             print(base_env_id, rewards)
             rewards = 0
             obs = env.reset()

fancy_gym/examples/examples_general.py

@@ -1,6 +1,6 @@
 from collections import defaultdict
 
-import gym
+import gymnasium as gym
 import numpy as np
 
 import fancy_gym
@@ -29,13 +29,13 @@ def example_general(env_id="Pendulum-v1", seed=1, iterations=1000, render=True):
 
     # number of environment steps
     for i in range(iterations):
-        obs, reward, done, info = env.step(env.action_space.sample())
+        obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
         rewards += reward
 
         if render:
             env.render()
 
-        if done:
+        if terminated or truncated:
             print(rewards)
             rewards = 0
             obs = env.reset()
@@ -69,12 +69,15 @@ def example_async(env_id="HoleReacher-v0", n_cpu=4, seed=int('533D', 16), n_samp
     # this would generate more samples than requested if n_samples % num_envs != 0
     repeat = int(np.ceil(n_samples / env.num_envs))
     for i in range(repeat):
-        obs, reward, done, info = env.step(env.action_space.sample())
+        obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
         buffer['obs'].append(obs)
         buffer['reward'].append(reward)
-        buffer['done'].append(done)
+        buffer['terminated'].append(terminated)
+        buffer['truncated'].append(truncated)
         buffer['info'].append(info)
         rewards += reward
+
+        done = terminated or truncated
         if np.any(done):
             print(f"Reward at iteration {i}: {rewards[done]}")
             rewards[done] = 0

fancy_gym/examples/examples_metaworld.py

@@ -29,9 +29,9 @@ def example_dmc(env_id="fish-swim", seed=1, iterations=1000, render=True):
             # THIS NEEDS TO BE SET TO FALSE FOR NOW, BECAUSE THE INTERFACE FOR RENDERING IS DIFFERENT TO BASIC GYM
             # TODO: Remove this, when Metaworld fixes its interface.
             env.render(False)
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
-        if done:
+        if terminated or truncated:
             print(env_id, rewards)
             rewards = 0
             obs = env.reset()
@@ -103,10 +103,10 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
     # number of samples/full trajectories (multiple environment steps)
     for i in range(iterations):
         ac = env.action_space.sample()
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
 
-        if done:
+        if terminated or truncated:
             print(base_env_id, rewards)
             rewards = 0
             obs = env.reset()
@@ -131,4 +131,3 @@ if __name__ == '__main__':
     #
     # # Custom MetaWorld task
     example_custom_dmc_and_mp(seed=10, iterations=1, render=render)
-

fancy_gym/examples/examples_movement_primitives.py

@@ -41,11 +41,11 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
         # This executes a full trajectory and gives back the context (obs) of the last step in the trajectory, or the
         # full observation space of the last step, if replanning/sub-trajectory learning is used. The 'reward' is equal
         # to the return of a trajectory. Default is the sum over the step-wise rewards.
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         # Aggregated returns
         returns += reward
 
-        if done:
+        if terminated or truncated:
             print(reward)
             obs = env.reset()
 
@@ -79,10 +79,10 @@ def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render
     # number of samples/full trajectories (multiple environment steps)
     for i in range(iterations):
         ac = env.action_space.sample()
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         returns += reward
 
-        if done:
+        if terminated or truncated:
             print(i, reward)
             obs = env.reset()
 
@@ -145,10 +145,10 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
     # number of samples/full trajectories (multiple environment steps)
     for i in range(iterations):
         ac = env.action_space.sample()
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
 
-        if done:
+        if terminated or truncated:
             print(rewards)
             rewards = 0
             obs = env.reset()

fancy_gym/examples/examples_open_ai.py

@@ -24,10 +24,10 @@ def example_mp(env_name, seed=1, render=True):
         else:
             env.render(mode=None)
         ac = env.action_space.sample()
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
         returns += reward
 
-        if done:
+        if terminated or truncated:
             print(returns)
             obs = env.reset()
 

fancy_gym/examples/pd_control_gain_tuning.py

@@ -34,7 +34,7 @@ fig.show()
 for t, pos_vel in enumerate(zip(pos, vel)):
     actions = env.tracking_controller.get_action(pos_vel[0], pos_vel[1], env.current_vel, env.current_pos)
     actions = np.clip(actions, env.env.action_space.low, env.env.action_space.high)
-    _, _, _, _ = env.env.step(actions)
+    env.env.step(actions)
     if t % 15 == 0:
         img.set_data(env.env.render(mode="rgb_array"))
         fig.canvas.draw()

setup.py

@@ -7,8 +7,10 @@ extras = {
     "dmc": ["dm_control>=1.0.1"],
     "metaworld": ["metaworld @ git+https://github.com/rlworkgroup/metaworld.git@master#egg=metaworld",
                   'mujoco-py<2.2,>=2.1',
-                  'scipy'
+                  'scipy',
+                  'gym>=0.15.4',
                   ],
+    "mujoco": ["gymnasium[mujoco]"],
 }
 
 # All dependencies
@@ -18,7 +20,7 @@ extras["all"] = list(set(itertools.chain.from_iterable(map(lambda group: extras[
 setup(
     author='Fabian Otto, Onur Celik',
     name='fancy_gym',
-    version='0.2',
+    version='0.3',
     classifiers=[
         # Python 3.7 is minimally supported
         "Programming Language :: Python :: 3",
@@ -29,7 +31,7 @@ setup(
     ],
     extras_require=extras,
     install_requires=[
-        'gym[mujoco]<0.25.0,>=0.24.0',
+        'gymnasium',
         'mp_pytorch @ git+https://github.com/ALRhub/MP_PyTorch.git@main'
     ],
     packages=[package for package in find_packages() if package.startswith("fancy_gym")],

test/test_dmc_envs.py

@@ -1,39 +1,43 @@
 from itertools import chain
+from typing import Callable
 
+import gymnasium as gym
 import pytest
 from dm_control import suite, manipulation
 
 import fancy_gym
 from test.utils import run_env, run_env_determinism
 
-SUITE_IDS = [f'dmc:{env}-{task}' for env, task in suite.ALL_TASKS if env != "lqr"]
-MANIPULATION_IDS = [f'dmc:manipulation-{task}' for task in manipulation.ALL if task.endswith('_features')]
+# SUITE_IDS = [f'dmc:{env}-{task}' for env, task in suite.ALL_TASKS if env != "lqr"]
+# MANIPULATION_IDS = [f'dmc:manipulation-{task}' for task in manipulation.ALL if task.endswith('_features')]
+DM_CONTROL_IDS = [spec.id for spec in gym.envs.registry.values() if
+                  not isinstance(spec.entry_point, Callable) and spec.entry_point.startswith('dm_control/')]
 DMC_MP_IDS = chain(*fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
 SEED = 1
 
 
-@pytest.mark.parametrize('env_id', SUITE_IDS)
-def test_step_suite_functionality(env_id: str):
+@pytest.mark.parametrize('env_id', DM_CONTROL_IDS)
+def test_step_dm_control_functionality(env_id: str):
     """Tests that suite step environments run without errors using random actions."""
     run_env(env_id)
 
 
-@pytest.mark.parametrize('env_id', SUITE_IDS)
-def test_step_suite_determinism(env_id: str):
+@pytest.mark.parametrize('env_id', DM_CONTROL_IDS)
+def test_step_dm_control_determinism(env_id: str):
     """Tests that for step environments identical seeds produce identical trajectories."""
     run_env_determinism(env_id, SEED)
 
 
-@pytest.mark.parametrize('env_id', MANIPULATION_IDS)
-def test_step_manipulation_functionality(env_id: str):
-    """Tests that manipulation step environments run without errors using random actions."""
-    run_env(env_id)
-
-
-@pytest.mark.parametrize('env_id', MANIPULATION_IDS)
-def test_step_manipulation_determinism(env_id: str):
-    """Tests that for step environments identical seeds produce identical trajectories."""
-    run_env_determinism(env_id, SEED)
+# @pytest.mark.parametrize('env_id', MANIPULATION_IDS)
+# def test_step_manipulation_functionality(env_id: str):
+#     """Tests that manipulation step environments run without errors using random actions."""
+#     run_env(env_id)
+#
+#
+# @pytest.mark.parametrize('env_id', MANIPULATION_IDS)
+# def test_step_manipulation_determinism(env_id: str):
+#     """Tests that for step environments identical seeds produce identical trajectories."""
+#     run_env_determinism(env_id, SEED)
 
 
 @pytest.mark.parametrize('env_id', DMC_MP_IDS)

test/test_fancy_envs.py

@@ -1,12 +1,14 @@
 import itertools
+from typing import Callable
 
 import fancy_gym
-import gym
+import gymnasium as gym
 import pytest
 
 from test.utils import run_env, run_env_determinism
 
 CUSTOM_IDS = [id for id, spec in gym.envs.registry.items() if
+              not isinstance(spec.entry_point, Callable) and
               "fancy_gym" in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
 CUSTOM_MP_IDS = itertools.chain(*fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
 SEED = 1

test/test_gym_envs.py

@@ -1,12 +1,12 @@
 from itertools import chain
 
-import gym
+import gymnasium as gym
 import pytest
 
 import fancy_gym
 from test.utils import run_env, run_env_determinism
 
-GYM_IDS = [spec.id for spec in gym.envs.registry.all() if
+GYM_IDS = [spec.id for spec in gym.envs.registry.values() if
            "fancy_gym" not in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
 GYM_MP_IDS = chain(*fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
 SEED = 1

test/utils.py

@@ -1,4 +1,4 @@
-import gym
+import gymnasium as gym
 import numpy as np
 from fancy_gym import make
 
@@ -15,16 +15,16 @@ def run_env(env_id, iterations=None, seed=0, render=False):
         seed: random seeding
         render: Render the episode
 
-    Returns: observations, rewards, dones, actions
+    Returns: observations, rewards, terminations, truncations, actions
 
     """
     env: gym.Env = make(env_id, seed=seed)
     rewards = []
     observations = []
     actions = []
-    dones = []
-    obs = env.reset()
-    print(obs.dtype)
+    terminations = []
+    truncations = []
+    obs, _ = env.reset()
     verify_observations(obs, env.observation_space, "reset()")
 
     iterations = iterations or (env.spec.max_episode_steps or 1)
@@ -36,26 +36,28 @@ def run_env(env_id, iterations=None, seed=0, render=False):
         ac = env.action_space.sample()
         actions.append(ac)
         # ac = np.random.uniform(env.action_space.low, env.action_space.high, env.action_space.shape)
-        obs, reward, done, info = env.step(ac)
+        obs, reward, terminated, truncated, info = env.step(ac)
 
         verify_observations(obs, env.observation_space, "step()")
         verify_reward(reward)
-        verify_done(done)
+        verify_done(terminated)
+        verify_done(truncated)
 
         rewards.append(reward)
-        dones.append(done)
+        terminations.append(terminated)
+        truncations.append(truncated)
 
         if render:
             env.render("human")
 
-        if done:
+        if terminated or truncated:
             break
 
-    assert done, "Done flag is not True after end of episode."
+    assert terminated or truncated, "Termination or truncation flag is not True after end of episode."
     observations.append(obs)
     env.close()
     del env
-    return np.array(observations), np.array(rewards), np.array(dones), np.array(actions)
+    return np.array(observations), np.array(rewards), np.array(terminations), np.array(truncations), np.array(actions)
 
 
 def run_env_determinism(env_id: str, seed: int):
@@ -63,11 +65,12 @@ def run_env_determinism(env_id: str, seed: int):
     traj2 = run_env(env_id, seed=seed)
     # Iterate over two trajectories, which should have the same state and action sequence
     for i, time_step in enumerate(zip(*traj1, *traj2)):
-        obs1, rwd1, done1, ac1, obs2, rwd2, done2, ac2 = time_step
+        obs1, rwd1, term1, trunc1, ac1, obs2, rwd2, term2, trunc2, ac2 = time_step
         assert np.array_equal(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match."
         assert np.array_equal(ac1, ac2), f"Actions [{i}] {ac1} and {ac2} do not match."
         assert np.array_equal(rwd1, rwd2), f"Rewards [{i}] {rwd1} and {rwd2} do not match."
-        assert np.array_equal(done1, done2), f"Dones [{i}] {done1} and {done2} do not match."
+        assert np.array_equal(term1, term2), f"Terminateds [{i}] {term1} and {term2} do not match."
+        assert np.array_equal(term1, term2), f"Truncateds [{i}] {trunc1} and {trunc2} do not match."
 
 
 def verify_observations(obs, observation_space: gym.Space, obs_type="reset()"):