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README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - CarRacing-v0
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: PPO
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: CarRacing-v0
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+ type: CarRacing-v0
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+ metrics:
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+ - type: mean_reward
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+ value: 588.34 +/- 233.48
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **PPO** Agent playing **CarRacing-v0**
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+ This is a trained model of a **PPO** agent playing **CarRacing-v0**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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+
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+ The RL Zoo is a training framework for Stable Baselines3
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+ reinforcement learning agents,
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+ with hyperparameter optimization and pre-trained agents included.
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+
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+ ## Usage (with SB3 RL Zoo)
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+
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+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+
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+ Install the RL Zoo (with SB3 and SB3-Contrib):
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+ ```bash
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+ pip install rl_zoo3
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+ ```
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+
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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m rl_zoo3.load_from_hub --algo ppo --env CarRacing-v0 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo ppo --env CarRacing-v0 -f logs/
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+ ```
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+
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+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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+ ```
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+ python -m rl_zoo3.load_from_hub --algo ppo --env CarRacing-v0 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo ppo --env CarRacing-v0 -f logs/
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+ ```
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+
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+ ## Training (with the RL Zoo)
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+ ```
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+ python -m rl_zoo3.train --algo ppo --env CarRacing-v0 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m rl_zoo3.push_to_hub --algo ppo --env CarRacing-v0 -f logs/ -orga qgallouedec
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+ ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('batch_size', 128),
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+ ('clip_range', 0.2),
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+ ('ent_coef', 0.0),
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+ ('env_wrapper',
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+ [{'rl_zoo3.wrappers.FrameSkip': {'skip': 2}},
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+ {'gym.wrappers.resize_observation.ResizeObservation': {'shape': 64}},
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+ {'gym.wrappers.gray_scale_observation.GrayScaleObservation': {'keep_dim': True}}]),
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+ ('frame_stack', 2),
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+ ('gae_lambda', 0.95),
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+ ('gamma', 0.99),
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+ ('learning_rate', 'lin_1e-4'),
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+ ('max_grad_norm', 0.5),
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+ ('n_envs', 8),
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+ ('n_epochs', 10),
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+ ('n_steps', 512),
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+ ('n_timesteps', 4000000.0),
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+ ('normalize', "{'norm_obs': False, 'norm_reward': True}"),
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+ ('policy', 'CnnPolicy'),
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+ ('policy_kwargs',
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+ 'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.GELU, '
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+ 'net_arch=dict(pi=[256], vf=[256]), )'),
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+ ('sde_sample_freq', 4),
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+ ('use_sde', True),
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+ ('vf_coef', 0.5),
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+ ('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})])
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+ ```
args.yml ADDED
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+ !!python/object/apply:collections.OrderedDict
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+ - - - algo
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+ - ppo
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+ - - conf_file
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+ - null
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+ - - device
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+ - auto
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+ - - env
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+ - CarRacing-v0
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+ - - env_kwargs
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+ - null
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+ - - eval_episodes
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+ - 20
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+ - - eval_freq
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+ - 25000
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+ - - gym_packages
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+ - []
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+ - - hyperparams
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+ - null
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+ - - log_folder
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+ - logs
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+ - - log_interval
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+ - -1
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+ - - max_total_trials
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+ - null
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+ - - n_eval_envs
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+ - 5
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+ - - n_evaluations
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+ - null
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+ - - n_jobs
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+ - 1
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+ - - n_startup_trials
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+ - 10
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+ - - n_timesteps
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+ - -1
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+ - - n_trials
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+ - 500
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+ - - no_optim_plots
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+ - false
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+ - - num_threads
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+ - -1
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+ - - optimization_log_path
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+ - null
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+ - - optimize_hyperparameters
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+ - false
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+ - - progress
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+ - false
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+ - - pruner
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+ - median
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+ - - sampler
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+ - tpe
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+ - - save_freq
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+ - -1
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+ - - save_replay_buffer
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+ - false
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+ - - seed
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+ - 2462650343
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+ - - storage
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+ - null
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+ - - study_name
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+ - null
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+ - - tensorboard_log
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+ - runs/CarRacing-v0__ppo__2462650343__1676837748
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+ - - track
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+ - true
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+ - - trained_agent
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+ - ''
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+ - - truncate_last_trajectory
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+ - true
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+ - - uuid
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+ - false
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+ - - vec_env
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+ - subproc
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+ - - verbose
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+ - 1
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+ - - wandb_entity
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+ - openrlbenchmark
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+ - - wandb_project_name
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+ - sb3
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+ - - wandb_tags
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+ - []
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+ - - yaml_file
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+ - null
config.yml ADDED
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+ !!python/object/apply:collections.OrderedDict
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+ - 128
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+ - - clip_range
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+ - 0.2
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+ - - ent_coef
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+ - 0.0
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+ - - env_wrapper
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+ - - rl_zoo3.wrappers.FrameSkip:
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+ skip: 2
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+ - gym.wrappers.resize_observation.ResizeObservation:
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+ shape: 64
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+ - gym.wrappers.gray_scale_observation.GrayScaleObservation:
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+ keep_dim: true
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+ - - frame_stack
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+ - 2
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+ - - gae_lambda
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+ - 0.95
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+ - - gamma
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+ - 0.99
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+ - - learning_rate
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+ - lin_1e-4
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+ - - max_grad_norm
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+ - 0.5
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+ - - n_envs
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+ - 8
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+ - - n_epochs
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+ - 10
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+ - - n_steps
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+ - 512
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+ - - n_timesteps
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+ - 4000000.0
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+ - - normalize
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+ - '{''norm_obs'': False, ''norm_reward'': True}'
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+ - - policy
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+ - CnnPolicy
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+ - - policy_kwargs
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+ - dict(log_std_init=-2, ortho_init=False, activation_fn=nn.GELU, net_arch=dict(pi=[256],
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+ vf=[256]), )
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+ - - sde_sample_freq
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+ - 4
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+ - - use_sde
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+ - true
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+ - - vf_coef
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+ - 0.5
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+ - OS: Linux-5.19.0-32-generic-x86_64-with-glibc2.35 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 30 17:03:34 UTC 2
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+ - Python: 3.9.12
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+ - Stable-Baselines3: 1.8.0a6
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+ - PyTorch: 1.13.1+cu117
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+ - GPU Enabled: True
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+ - Numpy: 1.24.1
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+ - Gym: 0.21.0
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