MTRF/r3l/r3l/r3l_agents/softlearning/evaluation_scripts/phased_evals_reorient.py [67:95]:
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        },
        init_euler_range_params={
            "type": "UniformRange",
            "values": [np.array([0, 0, -np.pi]), np.array([0, 0, np.pi])],
        },
        reset_every_n_episodes=1,
        reset_robot=True,
        readjust_to_object_in_reset=True,
    )
    env.reset()

    success_rates = []
    ckpt_numbers = []
    obs_dicts_per_policy = []
    rew_dicts_per_policy = []
    returns_per_policy = []

    for ckpt_dir in checkpoint_dirs[::EVAL_EVERY_N]:
        ckpt_number = ckpt_dir.split("_")[-1]
        print("EVALUATING CHECKPOINT: ", ckpt_number)
        policy = load_policy_from_checkpoint(ckpt_dir, env)

        successes = []
        obs_dicts = []
        rew_dicts = []
        returns = []
        frames = []
        for ep in range(N_EVAL_EPISODES):
            env.reset()
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MTRF/r3l/r3l/r3l_agents/softlearning/evaluation_scripts/phased_evals_reposition.py [69:97]:
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        },
        init_euler_range_params={
            "type": "UniformRange",
            "values": [np.array([0, 0, -np.pi]), np.array([0, 0, np.pi])],
        },
        reset_every_n_episodes=1,
        reset_robot=True,
        readjust_to_object_in_reset=True,
    )
    env.reset()

    success_rates = []
    ckpt_numbers = []
    obs_dicts_per_policy = []
    rew_dicts_per_policy = []
    returns_per_policy = []

    for ckpt_dir in checkpoint_dirs[::EVAL_EVERY_N]:
        ckpt_number = ckpt_dir.split("_")[-1]
        print("EVALUATING CHECKPOINT: ", ckpt_number)
        policy = load_policy_from_checkpoint(ckpt_dir, env)

        successes = []
        obs_dicts = []
        rew_dicts = []
        returns = []
        frames = []
        for ep in range(N_EVAL_EPISODES):
            env.reset()
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