# Round-Table Low
python -m src.train.train_ri \
    +experiment=rl/ri \
    actor=residual_mlp \
    base_policy.wandb_id=flare-robot/furniture_bc_mlp/0depffyt \
    base_policy.wt_type=best_success_rate \
    env.task=round_table \
    env.randomness=low \
    load_pretrained_wts=true \
    il_base_only=true \
    gpu_id=3 \
    num_env_steps=1000 \
    debug=false \
    initial_num_bc_epochs=10 \
    rl_per_bc=15 \
    +run_name=mlp_RI_M15_act-scale0.1

# One-Leg Low
# python -m src.train.train_ri \
#     +experiment=rl/ri \
#     actor=residual_mlp \
#     base_policy.wandb_id=flare-robot/furniture_bc_mlp/n7kw9kl1 \
#     base_policy.wt_type=best_success_rate \
#     env.task=one_leg \
#     env.randomness=low \
#     load_pretrained_wts=true \
#     il_base_only=false \
#     gpu_id=3 \
#     num_env_steps=700 \
#     debug=false \
#     initial_num_bc_epochs=0 \
#     rl_per_bc=5 \
#     +run_name=mlp_R

# - `one_leg`: 700
# - `lamp`: 1000
# - `round_table`: 1000
# - `mug_rack`: 400
# - `peg_hole`: 200 or 400?
