# One-leg Med
python -m src.train.train_ri \
    +experiment=rl/ri \
    base_policy.wandb_id=flare-robot/furniturebench_bc/sq3wyuo0 \
    base_policy.wt_type=best_success_rate \
    env.task=one_leg \
    env.randomness=med \
    il_base_only=true \
    gpu_id=2 \
    load_pretrained_wts=true \
    il_base_only=true \
    enable_q_filter=false \
    q_filter_min_weight=0.5 \
    enable_rl_replay=false \
    max_replay_new_samples=null \
    replay_from_sr=0.0 \
    base_bc.replay_buffer_size=30000 \
    num_env_steps=700 \
    debug=false \
    initial_num_bc_epochs=0 \
    rl_per_bc=15 \
    +run_name=R

# One-leg Med
# python -m src.train.train_ri \
#     +experiment=rl/ri \
#     base_policy.wandb_id=flare-robot/one_leg-state-low/tmlpw7oh \
#     base_policy.wt_type=best_success_rate \
#     env.task=one_leg \
#     env.randomness=low \
#     gpu_id=0 \
#     load_pretrained_wts=true \
#     il_base_only=false \
#     enable_q_filter=false \
#     q_filter_min_weight=0.5 \
#     enable_rl_replay=false \
#     max_replay_new_samples=null \
#     replay_from_sr=0.0 \
#     base_bc.replay_buffer_size=30000 \
#     num_env_steps=700 \
#     debug=false \
#     initial_num_bc_epochs=100 \
#     rl_per_bc=1 \
#     +run_name=RI_10ep_M1

# Round-table Low
# python -m src.train.train_ri \
#     +experiment=rl/ri \
#     base_policy.wandb_id=flare-robot/furniturebench_bc/93bghu4o \
#     env.randomness=low \
#     base_policy.wt_type=best_success_rate \
#     env.task=round_table \
#     gpu_id=1 \
#     load_pretrained_wts=true \
#     il_base_only=false \
#     enable_q_filter=false \
#     q_filter_min_weight=0.5 \
#     enable_rl_replay=false \
#     max_replay_new_samples=null \
#     replay_from_sr=0.0 \
#     base_bc.replay_buffer_size=30000 \
#     num_env_steps=1000 \
#     debug=false \
#     initial_num_bc_epochs=10 \
#     rl_per_bc=5 \
#     +run_name=RI_bc-ep-10_M0.2

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