19f6bfac9d39794db025dc53f8921346f58080b9,enjoy.py,,,#,12

Before Change

render_func = None
tmp_env = env
while True:
    if hasattr(tmp_env, "envs"):
        render_func = tmp_env.envs[0].render
        break
    elif hasattr(tmp_env, "venv"):
        tmp_env = tmp_env.venv
    elif hasattr(tmp_env, "env"):
        tmp_env = tmp_env.env
    else:
        break

// We need to use the same statistics for normalization as used in training
actor_critic, ob_rms = \
            torch.load(os.path.join(args.load_dir, args.env_name + ".pt"))

if isinstance(env.venv, VecNormalize):

After Change

vec_norm = get_vec_normalize(envs)
if vec_norm is not None:
    vec_norm.eval()
    vec_norm.ob_rms = ob_rms

recurrent_hidden_states = torch.zeros(1, actor_critic.recurrent_hidden_state_size)
masks = torch.zeros(1, 1)

Instances