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Merge pull request #91 from JuliusJacobitz/fix_readme_07_12_23

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add render_mode to examples in README to make them work again
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Onur 2023-12-22 10:07:54 +01:00 committed by GitHub
commit fa54dbb149
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1 changed files with 10 additions and 14 deletions
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README.md
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@ -105,17 +105,16 @@ Regular step based environments added by Fancy Gym are added into the `fancy/` n
import gymnasium as gym
import fancy_gym
env = gym.make('fancy/Reacher5d-v0')
# or env = gym.make('metaworld/reach-v2') # fancy_gym allows access to all metaworld ML1 tasks via the metaworld/ NS
# or env = gym.make('dm_control/ball_in_cup-catch-v0')
# or env = gym.make('Reacher-v2')
env = gym.make('fancy/Reacher5d-v0', render_mode='human')
# or env = gym.make('metaworld/reach-v2', render_mode='human') # fancy_gym allows access to all metaworld ML1 tasks via the metaworld/ NS
# or env = gym.make('dm_control/ball_in_cup-catch-v0', render_mode='human')
# or env = gym.make('Reacher-v2', render_mode='human')
observation = env.reset(seed=1)
env.render()
for i in range(1000):
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
if i % 5 == 0:
env.render()
if terminated or truncated:
observation, info = env.reset()
@ -149,17 +148,14 @@ Just keep in mind, calling `step()` executes a full trajectory.
import gymnasium as gym
import fancy_gym
env = gym.make('fancy_ProMP/Reacher5d-v0')
# or env = gym.make('metaworld_ProDMP/reach-v2')
# or env = gym.make('dm_control_DMP/ball_in_cup-catch-v0')
# or env = gym.make('gym_ProMP/Reacher-v2') # mp versions of envs added directly by gymnasium are in the gym_<MP-type> NS
# render() can be called once in the beginning with all necessary arguments.
# To turn it of again just call render() without any arguments.
env.render(mode='human')
env = gym.make('fancy_ProMP/Reacher5d-v0', render_mode="human")
# or env = gym.make('metaworld_ProDMP/reach-v2', render_mode="human")
# or env = gym.make('dm_control_DMP/ball_in_cup-catch-v0', render_mode="human")
# or env = gym.make('gym_ProMP/Reacher-v2', render_mode="human") # mp versions of envs added directly by gymnasium are in the gym_<MP-type> NS
# This returns the context information, not the full state observation
observation, info = env.reset(seed=1)
env.render()
for i in range(5):
action = env.action_space.sample()