Datasets:

SLM-Lab
/

benchmark

Tasks:

Reinforcement Learning

Modalities:

Formats:

Languages:

Size:

Tags:

reinforcement-learning

deep-learning

pytorch

Libraries:

Datasets

License:

Dataset card Data Studio Files Files and versions

xet

Community

benchmark / docs /BENCHMARKS.md

kengzwl

Upload folder using huggingface_hub

7692e18 verified 2 days ago

preview code

raw

history blame contribute delete

55.4 kB

SLM-Lab Benchmarks

Reproducible deep RL algorithm validation across Gymnasium environments (Classic Control, Box2D, MuJoCo, Atari).

Usage

After installation, copy SPEC_FILE and SPEC_NAME from result tables below (Atari uses one shared spec file - see Phase 4).

Running Benchmarks

Local - runs on your machine (Classic Control: minutes):

slm-lab run SPEC_FILE SPEC_NAME train

Remote - cloud GPU via dstack, auto-syncs to HuggingFace:

source .env && slm-lab run-remote --gpu SPEC_FILE SPEC_NAME train -n NAME

Remote setup: cp .env.example .env then set HF_TOKEN. See README for dstack config.

Atari

All games share one spec file (54 tested, 5 hard exploration skipped). Use -s env=ENV to substitute. Runs take ~2-3 hours on GPU.

source .env && slm-lab run-remote --gpu -s env=ALE/Pong-v5 slm_lab/spec/benchmark/ppo/ppo_atari.json ppo_atari train -n pong

Download Results

Trained models and metrics sync to HuggingFace. Pull locally:

source .env && slm-lab pull SPEC_NAME
slm-lab list  # see available experiments

Benchmark Contribution

To ensure benchmark integrity, follow these steps when adding or updating results:

1. Audit Spec Settings

Before Running: Ensure spec.json matches the Settings line defined in each benchmark table.
Example: max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500
After Pulling: Verify the downloaded spec.json matches these rules before using the data.

2. Run Benchmark & Commit Specs

Run: Execute the benchmark locally or remotely using the commands in Usage.
Commit Specs: Always commit the spec.json file used for the run to the repo.
Table Entry: Ensure BENCHMARKS.md has an entry with the correct SPEC_FILE and SPEC_NAME.

3. Record Scores & Plots

Score: At run completion, extract total_reward_ma from logs (trial_metrics).
Link: Add HuggingFace folder link: [FOLDER](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/FOLDER)
Pull data: source .env && uv run hf download SLM-Lab/benchmark --include "data/FOLDER/*" --local-dir hf_data --repo-type dataset

Plot: Generate with folders from table:

slm-lab plot -t "CartPole-v1" -f ppo_cartpole_2026...,dqn_cartpole_2026...

Environment Settings

Standardized settings for fair comparison. The Settings line in each result table shows these values.

Env Category	num_envs	max_frame	log_frequency	grace_period
Classic Control	4	2e5-3e5	500	1e4
Box2D	8	3e5	1000	5e4
MuJoCo	16	1e6-10e6	1e4	1e5-1e6
Atari	16	10e6	10000	5e5

Hyperparameter Search

When algorithm fails to reach target, run search instead of train:

slm-lab run SPEC_FILE SPEC_NAME search                                        # local
source .env && slm-lab run-remote --gpu SPEC_FILE SPEC_NAME search -n NAME    # remote

Stage	Mode	Config	Purpose
ASHA	`search`	`max_session=1`, `search_scheduler` enabled	Wide exploration with early stopping
Multi	`search`	`max_session=4`, NO `search_scheduler`	Robust validation with averaging
Validate	`train`	Final spec	Confirmation run

Do not use search result in benchmark results - use final validation run with committed spec.

Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20+ = 5+ dims).

{
  "meta": {
    "max_session": 1, "max_trial": 16,
    "search_resources": {"cpu": 1, "gpu": 0.125},
    "search_scheduler": {"grace_period": 1e5, "reduction_factor": 3}
  },
  "search": {
    "agent.algorithm.gamma__uniform": [0.98, 0.999],
    "agent.algorithm.lam__uniform": [0.9, 0.98],
    "agent.net.optim_spec.lr__loguniform": [1e-4, 1e-3]
  }
}

Progress

Phase	Category	Envs	REINFORCE	SARSA	DQN	DDQN+PER	A2C	PPO	SAC	Overall
1	Classic Control	3	🔄	🔄	🔄	🔄	🔄	🔄	🔄	Rerun pending
2	Box2D	2	N/A	N/A	🔄	🔄	🔄	🔄	🔄	Rerun pending
3	MuJoCo	11	N/A	N/A	N/A	N/A	🔄	🔄	🔄	Rerun pending
4	Atari	59	N/A	N/A	N/A	Skip	🔄	✅	N/A	54 games

Results

Phase 1: Classic Control

1.1 CartPole-v1

Docs: CartPole | State: Box(4) | Action: Discrete(2) | Target reward MA > 400

Settings: max_frame 2e5 | num_envs 4 | max_session 4 | log_frequency 500

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
REINFORCE	✅	469.68	slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json	reinforce_cartpole	reinforce_cartpole_2026_01_30_215510
SARSA	✅	421.58	slm_lab/spec/benchmark/sarsa/sarsa_cartpole.json	sarsa_boltzmann_cartpole	sarsa_boltzmann_cartpole_2026_01_30_215508
DQN	⚠️	188.07	slm_lab/spec/benchmark/dqn/dqn_cartpole.json	dqn_boltzmann_cartpole	dqn_boltzmann_cartpole_2026_01_30_215213
DDQN+PER	✅	432.88	slm_lab/spec/benchmark/dqn/dqn_cartpole.json	ddqn_per_boltzmann_cartpole	ddqn_per_boltzmann_cartpole_2026_01_30_215454
A2C	✅	499.73	slm_lab/spec/benchmark/a2c/a2c_gae_cartpole.json	a2c_gae_cartpole	a2c_gae_cartpole_2026_01_30_215337
PPO	✅	499.54	slm_lab/spec/benchmark/ppo/ppo_cartpole.json	ppo_cartpole	ppo_cartpole_2026_01_30_221924
SAC	⚠️	359.69	slm_lab/spec/benchmark/sac/sac_cartpole.json	sac_cartpole	sac_cartpole_2026_01_30_221934

1.2 Acrobot-v1

Docs: Acrobot | State: Box(6) | Action: Discrete(3) | Target reward MA > -100

Settings: max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
DQN	✅	-94.81	slm_lab/spec/benchmark/dqn/dqn_acrobot.json	dqn_boltzmann_acrobot	dqn_boltzmann_acrobot_2026_01_30_215429
DDQN+PER	✅	-85.17	slm_lab/spec/benchmark/dqn/ddqn_per_acrobot.json	ddqn_per_acrobot	ddqn_per_acrobot_2026_01_30_215436
A2C	✅	-83.75	slm_lab/spec/benchmark/a2c/a2c_gae_acrobot.json	a2c_gae_acrobot	a2c_gae_acrobot_2026_01_30_215413
PPO	✅	-81.43	slm_lab/spec/benchmark/ppo/ppo_acrobot.json	ppo_acrobot	ppo_acrobot_2026_01_30_215352
SAC	✅	-97.08	slm_lab/spec/benchmark/sac/sac_acrobot.json	sac_acrobot	sac_acrobot_2026_01_30_215401

1.3 Pendulum-v1

Docs: Pendulum | State: Box(3) | Action: Box(1) | Target reward MA > -200

Settings: max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
A2C	❌	-553.00	slm_lab/spec/benchmark/a2c/a2c_gae_pendulum.json	a2c_gae_pendulum	a2c_gae_pendulum_2026_01_30_215421
PPO	✅	-168.26	slm_lab/spec/benchmark/ppo/ppo_pendulum.json	ppo_pendulum	ppo_pendulum_2026_01_30_215944
SAC	✅	-152.33	slm_lab/spec/benchmark/sac/sac_pendulum.json	sac_pendulum	sac_pendulum_2026_01_30_215454

Phase 2: Box2D

2.1 LunarLander-v3 (Discrete)

Docs: LunarLander | State: Box(8) | Action: Discrete(4) | Target reward MA > 200

Settings: max_frame 3e5 | num_envs 8 | max_session 4 | log_frequency 1000

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
DQN	⚠️	183.64	slm_lab/spec/benchmark/dqn/dqn_lunar.json	dqn_concat_lunar	dqn_concat_lunar_2026_01_30_215529
DDQN+PER	✅	261.49	slm_lab/spec/benchmark/dqn/ddqn_per_lunar.json	ddqn_per_concat_lunar	ddqn_per_concat_lunar_2026_01_30_215532
A2C	❌	9.53	slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json	a2c_gae_lunar	a2c_gae_lunar_2026_01_30_215529
PPO	⚠️	159.02	slm_lab/spec/benchmark/ppo/ppo_lunar.json	ppo_lunar	ppo_lunar_2026_01_30_215550
SAC	❌	-75.43	slm_lab/spec/benchmark/sac/sac_lunar.json	sac_lunar	sac_lunar_2026_01_30_215552

2.2 LunarLander-v3 (Continuous)

Docs: LunarLander | State: Box(8) | Action: Box(2) | Target reward MA > 200

Settings: max_frame 3e5 | num_envs 8 | max_session 4 | log_frequency 1000

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
A2C	❌	-38.18	slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json	a2c_gae_lunar_continuous	a2c_gae_lunar_continuous_2026_01_30_215630
PPO	⚠️	165.48	slm_lab/spec/benchmark/ppo/ppo_lunar.json	ppo_lunar_continuous	ppo_lunar_continuous_2026_01_31_104549
SAC	✅	208.60	slm_lab/spec/benchmark/sac/sac_lunar.json	sac_lunar_continuous	sac_lunar_continuous_2026_01_31_104537

Phase 3: MuJoCo

Docs: MuJoCo environments | State/Action: Continuous | Target: Practical baselines (no official "solved" threshold)

Settings: max_frame 4e6-10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm: PPO only - SAC omitted (off-policy = heavy compute for systematic benchmarking). Network: MLP [256,256] tanh, orthogonal init.

Spec Variants: Two unified specs in ppo_mujoco.json, plus individual specs for edge cases.

SPEC_NAME	Envs	Key Config
ppo_mujoco	HalfCheetah, Walker, Humanoid, HumanoidStandup	gamma=0.99, lam=0.95
ppo_mujoco_longhorizon	Reacher, Pusher	gamma=0.997, lam=0.97
Individual specs	Hopper, Swimmer, Ant, IP, IDP	See spec files for tuned hyperparams

Reproduce: Copy ENV, SPEC_FILE, SPEC_NAME from table. Use -s max_frame= for all specs, add -s env= for unified specs:

# Unified specs (ppo_mujoco.json)
source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=MAX_FRAME \
  slm_lab/spec/benchmark/ppo/ppo_mujoco.json SPEC_NAME train -n NAME

# Individual specs (env hardcoded)
source .env && slm-lab run-remote --gpu -s max_frame=MAX_FRAME \
  slm_lab/spec/benchmark/ppo/SPEC_FILE SPEC_NAME train -n NAME

ENV	MAX_FRAME	SPEC_FILE	SPEC_NAME
HalfCheetah-v5	10e6	ppo_mujoco.json	ppo_mujoco
Walker2d-v5	10e6	ppo_mujoco.json	ppo_mujoco
Humanoid-v5	10e6	ppo_mujoco.json	ppo_mujoco
HumanoidStandup-v5	4e6	ppo_mujoco.json	ppo_mujoco
Hopper-v5	4e6	ppo_hopper.json	ppo_hopper
Swimmer-v5	4e6	ppo_swimmer.json	ppo_swimmer
Ant-v5	10e6	ppo_ant.json	ppo_ant
Reacher-v5	4e6	ppo_mujoco.json	ppo_mujoco_longhorizon
Pusher-v5	4e6	ppo_mujoco.json	ppo_mujoco_longhorizon
InvertedPendulum-v5	4e6	ppo_inverted_pendulum.json	ppo_inverted_pendulum
InvertedDoublePendulum-v5	10e6	ppo_inverted_double_pendulum.json	ppo_inverted_double_pendulum

3.1 Hopper-v5

Docs: Hopper | State: Box(11) | Action: Box(3) | Target reward MA ~ 2000

Settings: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	1972.38	slm_lab/spec/benchmark/ppo/ppo_hopper.json	ppo_hopper	ppo_hopper_2026_01_31_105438

3.2 HalfCheetah-v5

Docs: HalfCheetah | State: Box(17) | Action: Box(6) | Target reward MA > 5000

Settings: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	5851.70	slm_lab/spec/benchmark/ppo/ppo_mujoco.json	ppo_mujoco	ppo_mujoco_halfcheetah_2026_01_30_230302

3.3 Walker2d-v5

Docs: Walker2d | State: Box(17) | Action: Box(6) | Target reward MA > 3500

Settings: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	4042.07	slm_lab/spec/benchmark/ppo/ppo_mujoco.json	ppo_mujoco	ppo_mujoco_walker2d_2026_01_30_222124

3.4 Ant-v5

Docs: Ant | State: Box(105) | Action: Box(8) | Target reward MA > 2000

Settings: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	2514.64	slm_lab/spec/benchmark/ppo/ppo_ant.json	ppo_ant	ppo_ant_2026_01_31_042006

3.5 Swimmer-v5

Docs: Swimmer | State: Box(8) | Action: Box(2) | Target reward MA > 200

Settings: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	229.31	slm_lab/spec/benchmark/ppo/ppo_swimmer.json	ppo_swimmer	ppo_swimmer_2026_01_30_215922

3.6 Reacher-v5

Docs: Reacher | State: Box(10) | Action: Box(2) | Target reward MA > -10

Settings: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	-5.08	slm_lab/spec/benchmark/ppo/ppo_mujoco.json	ppo_mujoco_longhorizon	ppo_mujoco_longhorizon_reacher_2026_01_30_215805

3.7 Pusher-v5

Docs: Pusher | State: Box(23) | Action: Box(7) | Target reward MA > -50

Settings: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	-49.09	slm_lab/spec/benchmark/ppo/ppo_mujoco.json	ppo_mujoco_longhorizon	ppo_mujoco_longhorizon_pusher_2026_01_30_215824

3.8 InvertedPendulum-v5

Docs: InvertedPendulum | State: Box(4) | Action: Box(1) | Target reward MA ~1000

Settings: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	944.87	slm_lab/spec/benchmark/ppo/ppo_inverted_pendulum.json	ppo_inverted_pendulum	ppo_inverted_pendulum_2026_01_30_230211

3.9 InvertedDoublePendulum-v5

Docs: InvertedDoublePendulum | State: Box(9) | Action: Box(1) | Target reward MA ~8000

Settings: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	7622.00	slm_lab/spec/benchmark/ppo/ppo_inverted_double_pendulum.json	ppo_inverted_double_pendulum	ppo_inverted_double_pendulum_2026_01_30_220651

3.10 Humanoid-v5

Docs: Humanoid | State: Box(348) | Action: Box(17) | Target reward MA > 1000

Settings: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	3774.08	slm_lab/spec/benchmark/ppo/ppo_mujoco.json	ppo_mujoco	ppo_mujoco_humanoid_2026_01_30_222339

3.11 HumanoidStandup-v5

Docs: HumanoidStandup | State: Box(348) | Action: Box(17) | Target reward MA > 100000

Settings: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4

Algorithm	Status	MA	SPEC_FILE	SPEC_NAME	HF Repo
PPO	✅	165841.17	slm_lab/spec/benchmark/ppo/ppo_mujoco.json	ppo_mujoco	ppo_mujoco_humanoidstandup_2026_01_30_215802

Phase 4: Atari

Docs: Atari environments | State: Box(84,84,4 after preprocessing) | Action: Discrete(4-18, game-dependent)

Settings: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 10000

Environment:

Gymnasium ALE v5 with life_loss_info=true
v5 uses sticky actions (repeat_action_probability=0.25) per Machado et al. (2018) best practices

Algorithm Specs (all use Nature CNN [32,64,64] + 512fc):

DDQN+PER: Skipped - off-policy variants ~~6x slower (~~230 fps vs ~1500 fps), not cost effective at 10M frames
A2C: a2c_gae_atari.json - RMSprop (lr=7e-4), training_frequency=32
PPO: ppo_atari.json - AdamW (lr=2.5e-4), minibatch=256, horizon=128, epochs=4

PPO Lambda Variants (table shows best result per game):

SPEC_NAME	Lambda	Best for
ppo_atari	0.95	Strategic games (default)
ppo_atari_lam85	0.85	Mixed games
ppo_atari_lam70	0.70	Action games

Reproduce:

# A2C
source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=1e7 \
  slm_lab/spec/benchmark/a2c/a2c_gae_atari.json a2c_gae_atari train -n NAME

# PPO
source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=1e7 \
  slm_lab/spec/benchmark/ppo/ppo_atari.json SPEC_NAME train -n NAME

ENV	Score	SPEC_NAME	HF Repo
ALE/AirRaid-v5	5067	a2c_gae_atari	a2c_gae_atari_airraid_2026_02_01_082446
	8245	ppo_atari	ppo_atari_airraid_2026_01_06_113119
ALE/Alien-v5	1488	a2c_gae_atari	a2c_gae_atari_alien_2026_02_01_000858
	1453	ppo_atari	ppo_atari_alien_2026_01_06_112514
ALE/Amidar-v5	330	a2c_gae_atari	a2c_gae_atari_amidar_2026_02_01_082251
	580	ppo_atari_lam85	ppo_atari_lam85_amidar_2026_01_07_223416
ALE/Assault-v5	1646	a2c_gae_atari	a2c_gae_atari_assault_2026_02_01_082252
	4293	ppo_atari_lam85	ppo_atari_lam85_assault_2026_01_08_130044
ALE/Asterix-v5	2712	a2c_gae_atari	a2c_gae_atari_asterix_2026_02_01_082315
	3482	ppo_atari_lam85	ppo_atari_lam85_asterix_2026_01_07_223445
ALE/Asteroids-v5	2106	a2c_gae_atari	a2c_gae_atari_asteroids_2026_02_01_082328
	1554	ppo_atari_lam85	ppo_atari_lam85_asteroids_2026_01_07_224245
ALE/Atlantis-v5	873365	a2c_gae_atari	a2c_gae_atari_atlantis_2026_02_01_082330
	792886	ppo_atari	ppo_atari_atlantis_2026_01_06_120440
ALE/BankHeist-v5	1099	a2c_gae_atari	a2c_gae_atari_bankheist_2026_02_01_082403
	1045	ppo_atari	ppo_atari_bankheist_2026_01_06_121042
ALE/BattleZone-v5	2437	a2c_gae_atari	a2c_gae_atari_battlezone_2026_02_01_082425
	26383	ppo_atari_lam85	ppo_atari_lam85_battlezone_2026_01_08_094729
ALE/BeamRider-v5	2767	a2c_gae_atari	a2c_gae_atari_beamrider_2026_02_01_000921
	2765	ppo_atari	ppo_atari_beamrider_2026_01_06_112533
ALE/Berzerk-v5	439	a2c_gae_atari	a2c_gae_atari_berzerk_2026_02_01_082540
	1072	ppo_atari	ppo_atari_berzerk_2026_01_06_112515
ALE/Bowling-v5	23.96	a2c_gae_atari	a2c_gae_atari_bowling_2026_02_01_082529
	46.45	ppo_atari	ppo_atari_bowling_2026_01_06_113148
ALE/Boxing-v5	1.80	a2c_gae_atari	a2c_gae_atari_boxing_2026_02_01_082539
	91.17	ppo_atari	ppo_atari_boxing_2026_01_06_112531
ALE/Breakout-v5	273	a2c_gae_atari	a2c_gae_atari_breakout_2026_01_31_213610
	327	ppo_atari_lam70	ppo_atari_lam70_breakout_2026_01_07_110559
ALE/Carnival-v5	2170	a2c_gae_atari	a2c_gae_atari_carnival_2026_02_01_082726
	3967	ppo_atari_lam70	ppo_atari_lam70_carnival_2026_01_07_144738
ALE/Centipede-v5	1382	a2c_gae_atari	a2c_gae_atari_centipede_2026_02_01_082643
	4915	ppo_atari_lam70	ppo_atari_lam70_centipede_2026_01_07_223557
ALE/ChopperCommand-v5	2446	a2c_gae_atari	a2c_gae_atari_choppercommand_2026_02_01_082626
	5355	ppo_atari	ppo_atari_choppercommand_2026_01_07_110539
ALE/CrazyClimber-v5	96943	a2c_gae_atari	a2c_gae_atari_crazyclimber_2026_02_01_082625
	107370	ppo_atari_lam85	ppo_atari_lam85_crazyclimber_2026_01_07_223609
ALE/Defender-v5	33149	a2c_gae_atari	a2c_gae_atari_defender_2026_02_01_082658
	51439	ppo_atari_lam70	ppo_atari_lam70_defender_2026_01_07_205238
ALE/DemonAttack-v5	2962	a2c_gae_atari	a2c_gae_atari_demonattack_2026_02_01_082717
	16558	ppo_atari_lam70	ppo_atari_lam70_demonattack_2026_01_07_111315
ALE/DoubleDunk-v5	-1.69	a2c_gae_atari	a2c_gae_atari_doubledunk_2026_02_01_082901
	-2.38	ppo_atari	ppo_atari_doubledunk_2026_01_07_110802
ALE/ElevatorAction-v5	731	a2c_gae_atari	a2c_gae_atari_elevatoraction_2026_02_01_082908
	5446	ppo_atari	ppo_atari_elevatoraction_2026_01_06_113129
ALE/Enduro-v5	681	a2c_gae_atari	a2c_gae_atari_enduro_2026_02_01_001123
	898	ppo_atari_lam85	ppo_atari_lam85_enduro_2026_01_08_095448
ALE/FishingDerby-v5	-16.38	a2c_gae_atari	a2c_gae_atari_fishingderby_2026_02_01_082906
	27.10	ppo_atari_lam85	ppo_atari_lam85_fishingderby_2026_01_08_094158
ALE/Freeway-v5	23.13	a2c_gae_atari	a2c_gae_atari_freeway_2026_02_01_082931
	31.30	ppo_atari	ppo_atari_freeway_2026_01_06_182318
ALE/Frostbite-v5	266	a2c_gae_atari	a2c_gae_atari_frostbite_2026_02_01_082915
	301	ppo_atari	ppo_atari_frostbite_2026_01_06_112556
ALE/Gopher-v5	984	a2c_gae_atari	a2c_gae_atari_gopher_2026_02_01_133323
	6508	ppo_atari_lam70	ppo_atari_lam70_gopher_2026_01_07_170451
ALE/Gravitar-v5	270	a2c_gae_atari	a2c_gae_atari_gravitar_2026_02_01_133244
	599	ppo_atari	ppo_atari_gravitar_2026_01_06_112548
ALE/Hero-v5	18680	a2c_gae_atari	a2c_gae_atari_hero_2026_02_01_175903
	28238	ppo_atari_lam85	ppo_atari_lam85_hero_2026_01_07_223619
ALE/IceHockey-v5	-5.92	a2c_gae_atari	a2c_gae_atari_icehockey_2026_02_01_175745
	-3.93	ppo_atari	ppo_atari_icehockey_2026_01_06_183721
ALE/Jamesbond-v5	460	a2c_gae_atari	a2c_gae_atari_jamesbond_2026_02_01_175945
	662	ppo_atari	ppo_atari_jamesbond_2026_01_06_183717
ALE/JourneyEscape-v5	-965	a2c_gae_atari	a2c_gae_atari_journeyescape_2026_02_01_084415
	-1252	ppo_atari_lam85	ppo_atari_lam85_journeyescape_2026_01_08_094842
ALE/Kangaroo-v5	322	a2c_gae_atari	a2c_gae_atari_kangaroo_2026_02_01_084415
	9912	ppo_atari_lam85	ppo_atari_lam85_kangaroo_2026_01_07_110838
ALE/Krull-v5	7519	a2c_gae_atari	a2c_gae_atari_krull_2026_02_01_084420
	7841	ppo_atari	ppo_atari_krull_2026_01_07_110747
ALE/KungFuMaster-v5	23006	a2c_gae_atari	a2c_gae_atari_kungfumaster_2026_02_01_085101
	29068	ppo_atari_lam70	ppo_atari_lam70_kungfumaster_2026_01_07_111317
ALE/MsPacman-v5	2110	a2c_gae_atari	a2c_gae_atari_mspacman_2026_02_01_001100
	2372	ppo_atari_lam85	ppo_atari_lam85_mspacman_2026_01_07_223522
ALE/NameThisGame-v5	5412	a2c_gae_atari	a2c_gae_atari_namethisgame_2026_02_01_132733
	5993	ppo_atari	ppo_atari_namethisgame_2026_01_06_182952
ALE/Phoenix-v5	5635	a2c_gae_atari	a2c_gae_atari_phoenix_2026_02_01_085101
	15659	ppo_atari_lam70	ppo_atari_lam70_phoenix_2026_01_07_110832
ALE/Pong-v5	10.17	a2c_gae_atari	a2c_gae_atari_pong_2026_01_31_213635
	16.91	ppo_atari_lam85	ppo_atari_lam85_pong_2026_01_08_094454
ALE/Pooyan-v5	2997	a2c_gae_atari	a2c_gae_atari_pooyan_2026_02_01_132748
	5716	ppo_atari_lam70	ppo_atari_lam70_pooyan_2026_01_07_224346
ALE/Qbert-v5	12619	a2c_gae_atari	a2c_gae_atari_qbert_2026_01_31_213720
	15094	ppo_atari	ppo_atari_qbert_2026_01_06_111801
ALE/Riverraid-v5	6558	a2c_gae_atari	a2c_gae_atari_riverraid_2026_02_01_132507
	9428	ppo_atari_lam85	ppo_atari_lam85_riverraid_2026_01_07_204356
ALE/RoadRunner-v5	29810	a2c_gae_atari	a2c_gae_atari_roadrunner_2026_02_01_132509
	37015	ppo_atari_lam85	ppo_atari_lam85_roadrunner_2026_01_07_145913
ALE/Robotank-v5	2.80	a2c_gae_atari	a2c_gae_atari_robotank_2026_02_01_132434
	20.07	ppo_atari	ppo_atari_robotank_2026_01_06_183413
ALE/Seaquest-v5	850	a2c_gae_atari	a2c_gae_atari_seaquest_2026_02_01_001001
	1796	ppo_atari	ppo_atari_seaquest_2026_01_06_183440
ALE/Skiing-v5	-14235	a2c_gae_atari	a2c_gae_atari_skiing_2026_02_01_132451
	-19340	ppo_atari	ppo_atari_skiing_2026_01_06_183424
ALE/Solaris-v5	2224	a2c_gae_atari	a2c_gae_atari_solaris_2026_02_01_212137
	2094	ppo_atari	ppo_atari_solaris_2026_01_06_192643
ALE/SpaceInvaders-v5	784	a2c_gae_atari	a2c_gae_atari_spaceinvaders_2026_02_01_000950
	726	ppo_atari	ppo_atari_spaceinvaders_2026_01_07_102346
ALE/StarGunner-v5	8665	a2c_gae_atari	a2c_gae_atari_stargunner_2026_02_01_132406
	47495	ppo_atari_lam70	ppo_atari_lam70_stargunner_2026_01_07_111404
ALE/Surround-v5	-9.72	a2c_gae_atari	a2c_gae_atari_surround_2026_02_01_132215
	-2.52	ppo_atari	ppo_atari_surround_2026_01_07_102404
ALE/Tennis-v5	-2873	a2c_gae_atari	a2c_gae_atari_tennis_2026_02_01_175829
	-4.41	ppo_atari_lam85	ppo_atari_lam85_tennis_2026_01_07_223532
ALE/TimePilot-v5	3376	a2c_gae_atari	a2c_gae_atari_timepilot_2026_02_01_175930
	4668	ppo_atari	ppo_atari_timepilot_2026_01_07_101010
ALE/Tutankham-v5	167	a2c_gae_atari	a2c_gae_atari_tutankham_2026_02_01_132347
	217	ppo_atari_lam85	ppo_atari_lam85_tutankham_2026_01_08_095251
ALE/UpNDown-v5	57099	a2c_gae_atari	a2c_gae_atari_upndown_2026_02_01_132435
	182472	ppo_atari	ppo_atari_upndown_2026_01_07_105708
ALE/VideoPinball-v5	25310	a2c_gae_atari	a2c_gae_atari_videopinball_2026_02_01_083457
	56746	ppo_atari_lam70	ppo_atari_lam70_videopinball_2026_01_07_224359
ALE/WizardOfWor-v5	2682	a2c_gae_atari	a2c_gae_atari_wizardofwor_2026_02_01_132449
	5814	ppo_atari	ppo_atari_wizardofwor_2026_01_06_221154
ALE/YarsRevenge-v5	24371	a2c_gae_atari	a2c_gae_atari_yarsrevenge_2026_02_01_132224
	17120	ppo_atari	ppo_atari_yarsrevenge_2026_01_06_221154
ALE/Zaxxon-v5	29.46	a2c_gae_atari	a2c_gae_atari_zaxxon_2026_02_01_131758
	10756	ppo_atari	ppo_atari_zaxxon_2026_01_06_221154

Training Curves (A2C vs PPO):

Skipped (hard exploration): Adventure, MontezumaRevenge, Pitfall, PrivateEye, Venture

PPO Lambda Comparison (click to expand)

ENV	ppo_atari	ppo_atari_lam85	ppo_atari_lam70
ALE/AirRaid-v5	8245	-	-
ALE/Alien-v5	1453	1353	1274
ALE/Amidar-v5	574	580	-
ALE/Assault-v5	4059	4293	3314
ALE/Asterix-v5	2967	3482	-
ALE/Asteroids-v5	1497	1554	-
ALE/Atlantis-v5	792886	754k	710k
ALE/BankHeist-v5	1045	1045	-
ALE/BattleZone-v5	21270	26383	13857
ALE/BeamRider-v5	2765	-	-
ALE/Berzerk-v5	1072	-	-
ALE/Bowling-v5	46.45	-	-
ALE/Boxing-v5	91.17	-	-
ALE/Breakout-v5	191	292	327
ALE/Carnival-v5	3071	3013	3967
ALE/Centipede-v5	3917	-	4915
ALE/ChopperCommand-v5	5355	-	-
ALE/CrazyClimber-v5	107183	107370	-
ALE/Defender-v5	37162	-	51439
ALE/DemonAttack-v5	7755	-	16558
ALE/DoubleDunk-v5	-2.38	-	-
ALE/ElevatorAction-v5	5446	363	3933
ALE/Enduro-v5	414	898	872
ALE/FishingDerby-v5	22.80	27.10	-
ALE/Freeway-v5	31.30	-	-
ALE/Frostbite-v5	301	275	267
ALE/Gopher-v5	4172	-	6508
ALE/Gravitar-v5	599	253	145
ALE/Hero-v5	21052	28238	-
ALE/IceHockey-v5	-3.93	-5.58	-7.36
ALE/Jamesbond-v5	662	-	-
ALE/JourneyEscape-v5	-1582	-1252	-1547
ALE/Kangaroo-v5	2623	9912	-
ALE/Krull-v5	7841	-	-
ALE/KungFuMaster-v5	18973	28334	29068
ALE/MsPacman-v5	2308	2372	2297
ALE/NameThisGame-v5	5993	-	-
ALE/Phoenix-v5	7940	-	15659
ALE/Pong-v5	15.01	16.91	12.85
ALE/Pooyan-v5	4704	-	5716
ALE/Qbert-v5	15094	-	-
ALE/Riverraid-v5	7319	9428	-
ALE/RoadRunner-v5	24204	37015	-
ALE/Robotank-v5	20.07	8.24	2.59
ALE/Seaquest-v5	1796	-	-
ALE/Skiing-v5	-19340	-22980	-29975
ALE/Solaris-v5	2094	-	-
ALE/SpaceInvaders-v5	726	-	-
ALE/StarGunner-v5	31862	-	47495
ALE/Surround-v5	-2.52	-	-6.79
ALE/Tennis-v5	-7.66	-4.41	-
ALE/TimePilot-v5	4668	-	-
ALE/Tutankham-v5	203	217	-
ALE/UpNDown-v5	182472	-	-
ALE/VideoPinball-v5	31385	-	56746
ALE/WizardOfWor-v5	5814	5466	4740
ALE/YarsRevenge-v5	17120	-	-
ALE/Zaxxon-v5	10756	-	-

Legend: Bold = Best score | - = Not tested