Files
tensorneat-mend/tensorneat/problem/rl/brax.py
2024-07-11 19:34:12 +08:00

84 lines
2.3 KiB
Python

import jax.numpy as jnp
from brax import envs
from .rl_jit import RLEnv
class BraxEnv(RLEnv):
def __init__(
self, env_name: str = "ant", backend: str = "generalized", *args, **kwargs
):
super().__init__(*args, **kwargs)
self.env_name = env_name
self.env = envs.create(env_name=env_name, backend=backend)
def env_step(self, randkey, env_state, action):
state = self.env.step(env_state, action)
return state.obs, state, state.reward, state.done.astype(jnp.bool_), state.info
def env_reset(self, randkey):
init_state = self.env.reset(randkey)
return init_state.obs, init_state
@property
def input_shape(self):
return (self.env.observation_size,)
@property
def output_shape(self):
return (self.env.action_size,)
def show(
self,
state,
randkey,
act_func,
params,
save_path=None,
height=480,
width=480,
*args,
**kwargs,
):
import jax
import imageio
from brax.io import image
obs, env_state = self.reset(randkey)
reward, done = 0.0, False
state_histories = [env_state.pipeline_state]
def step(key, env_state, obs):
key, _ = jax.random.split(key)
if self.action_policy is not None:
forward_func = lambda obs: act_func(state, params, obs)
action = self.action_policy(key, forward_func, obs)
else:
action = act_func(state, params, obs)
obs, env_state, r, done, _ = self.step(randkey, env_state, action)
return key, env_state, obs, r, done
jit_step = jax.jit(step)
for _ in range(self.max_step):
key, env_state, obs, r, done = jit_step(randkey, env_state, obs)
state_histories.append(env_state.pipeline_state)
reward += r
if done:
break
imgs = image.render_array(
sys=self.env.sys, trajectory=state_histories, height=height, width=width
)
if save_path is None:
save_path = f"{self.env_name}.gif"
imageio.mimsave(save_path, imgs, *args, **kwargs)
print("Gif saved to: ", save_path)
print("Total reward: ", reward)