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@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sf_log.txt filter=lfs diff=lfs merge=lfs -text

.summary/0/events.out.tfevents.1657324804.andrew-gpu

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+size 89172510

README.md

@@ -0,0 +1,11 @@
+---
+library_name: sample-factory
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- sample-factory
+---
+
+A(n) **APPO** model trained on the **quadrotor_multi** environment.
+This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
+    

cfg.json

@@ -0,0 +1,210 @@
+{
+  "algo": "APPO",
+  "env": "quadrotor_multi",
+  "experiment": "00_baseline_see_0",
+  "experiments_root": "quad_single_baseline/baseline_",
+  "help": false,
+  "train_dir": "./train_dir",
+  "device": "gpu",
+  "seed": 0,
+  "save_every_sec": 120,
+  "keep_checkpoints": 3,
+  "save_milestones_sec": 10000,
+  "stats_avg": 100,
+  "learning_rate": 0.0001,
+  "lr_schedule": "constant",
+  "lr_schedule_kl_threshold": 0.008,
+  "train_for_env_steps": 10000000000,
+  "train_for_seconds": 10000000000,
+  "obs_subtract_mean": 0.0,
+  "obs_scale": 1.0,
+  "gamma": 0.99,
+  "reward_scale": 1.0,
+  "reward_clip": 10.0,
+  "encoder_type": "mlp",
+  "encoder_subtype": "mlp_quads",
+  "encoder_custom": "quad_multi_encoder",
+  "encoder_extra_fc_layers": 0,
+  "hidden_size": 16,
+  "nonlinearity": "tanh",
+  "policy_initialization": "xavier_uniform",
+  "policy_init_gain": 1.0,
+  "actor_critic_share_weights": false,
+  "use_spectral_norm": false,
+  "adaptive_stddev": false,
+  "initial_stddev": 1.0,
+  "experiment_summaries_interval": 20,
+  "adam_eps": 1e-06,
+  "adam_beta1": 0.9,
+  "adam_beta2": 0.999,
+  "gae_lambda": 1.0,
+  "rollout": 128,
+  "num_workers": 36,
+  "recurrence": 1,
+  "use_rnn": false,
+  "rnn_type": "gru",
+  "rnn_num_layers": 1,
+  "ppo_clip_ratio": 0.1,
+  "ppo_clip_value": 5.0,
+  "batch_size": 1024,
+  "num_batches_per_iteration": 1,
+  "ppo_epochs": 1,
+  "num_minibatches_to_accumulate": -1,
+  "max_grad_norm": 5.0,
+  "exploration_loss_coeff": 0.0,
+  "value_loss_coeff": 0.5,
+  "kl_loss_coeff": 0.0,
+  "exploration_loss": "entropy",
+  "max_entropy_coeff": 0.0,
+  "num_envs_per_worker": 4,
+  "worker_num_splits": 2,
+  "num_policies": 1,
+  "policy_workers_per_policy": 1,
+  "max_policy_lag": 100000000,
+  "traj_buffers_excess_ratio": 1.3,
+  "decorrelate_experience_max_seconds": 10,
+  "decorrelate_envs_on_one_worker": true,
+  "with_vtrace": false,
+  "vtrace_rho": 1.0,
+  "vtrace_c": 1.0,
+  "set_workers_cpu_affinity": true,
+  "force_envs_single_thread": true,
+  "reset_timeout_seconds": 120,
+  "default_niceness": 0,
+  "train_in_background_thread": true,
+  "learner_main_loop_num_cores": 1,
+  "actor_worker_gpus": [],
+  "with_pbt": false,
+  "pbt_mix_policies_in_one_env": true,
+  "pbt_period_env_steps": 5000000,
+  "pbt_start_mutation": 20000000,
+  "pbt_replace_fraction": 0.3,
+  "pbt_mutation_rate": 0.15,
+  "pbt_replace_reward_gap": 0.1,
+  "pbt_replace_reward_gap_absolute": 1e-06,
+  "pbt_optimize_batch_size": false,
+  "pbt_optimize_gamma": false,
+  "pbt_target_objective": "true_reward",
+  "pbt_perturb_min": 1.05,
+  "pbt_perturb_max": 1.5,
+  "use_cpc": false,
+  "cpc_forward_steps": 8,
+  "cpc_time_subsample": 6,
+  "cpc_forward_subsample": 2,
+  "with_wandb": true,
+  "wandb_user": null,
+  "wandb_project": "sample_factory",
+  "wandb_group": null,
+  "wandb_job_type": "SF",
+  "wandb_tags": [
+    "swarm-rl",
+    "sf1",
+    "single"
+  ],
+  "benchmark": false,
+  "sampler_only": false,
+  "env_frameskip": 1,
+  "env_framestack": 4,
+  "pixel_format": "CHW",
+  "quads_discretize_actions": -1,
+  "quads_clip_input": false,
+  "quads_effort_reward": null,
+  "quads_episode_duration": 15.0,
+  "quads_num_agents": 1,
+  "quads_neighbor_hidden_size": 256,
+  "quads_neighbor_encoder_type": "no_encoder",
+  "quads_collision_reward": 5.0,
+  "quads_collision_obstacle_reward": 0.0,
+  "quads_settle_reward": 0.0,
+  "quads_settle": false,
+  "quads_vel_reward_out_range": 0.8,
+  "quads_settle_range_meters": 1.0,
+  "quads_collision_hitbox_radius": 2.0,
+  "quads_collision_falloff_radius": 4.0,
+  "quads_collision_smooth_max_penalty": 10.0,
+  "neighbor_obs_type": "none",
+  "quads_use_numba": true,
+  "quads_obstacle_mode": "no_obstacles",
+  "quads_obstacle_num": 0,
+  "quads_obstacle_type": "sphere",
+  "quads_obstacle_size": 0.0,
+  "quads_obstacle_traj": "gravity",
+  "quads_local_obs": -1,
+  "quads_local_coeff": 1.0,
+  "quads_local_metric": "dist",
+  "quads_view_mode": "local",
+  "quads_adaptive_env": false,
+  "quads_mode": "mix",
+  "quads_formation": "circle_horizontal",
+  "quads_formation_size": 0.0,
+  "room_dims": [
+    10,
+    10,
+    10
+  ],
+  "quads_obs_repr": "xyz_vxyz_R_omega",
+  "replay_buffer_sample_prob": 0.0,
+  "anneal_collision_steps": 0.0,
+  "quads_obstacle_obs_mode": "relative",
+  "quads_obstacle_hidden_size": 32,
+  "quads_collision_obst_smooth_max_penalty": 10.0,
+  "quads_obst_penalty_fall_off": 10.0,
+  "command_line": "--env=quadrotor_multi --train_for_env_steps=1000000000 --algo=APPO --use_rnn=False --num_workers=36 --num_envs_per_worker=4 --learning_rate=0.0001 --ppo_clip_value=5.0 --recurrence=1 --nonlinearity=tanh --actor_critic_share_weights=False --policy_initialization=xavier_uniform --adaptive_stddev=False --with_vtrace=False --max_policy_lag=100000000 --hidden_size=256 --gae_lambda=1.00 --max_grad_norm=5.0 --exploration_loss_coeff=0.0 --rollout=128 --batch_size=1024 --quads_use_numba=True --quads_mode=mix --quads_episode_duration=15.0 --quads_formation_size=0.0 --encoder_custom=quad_multi_encoder --with_pbt=False --quads_collision_reward=5.0 --quads_neighbor_hidden_size=256 --neighbor_obs_type=pos_vel --quads_settle_reward=0.0 --quads_collision_hitbox_radius=2.0 --quads_collision_falloff_radius=4.0 --quads_local_obs=6 --quads_local_metric=dist --quads_local_coeff=1.0 --quads_num_agents=8 --quads_collision_reward=5.0 --quads_collision_smooth_max_penalty=10.0 --quads_neighbor_encoder_type=attention --replay_buffer_sample_prob=0.75 --anneal_collision_steps=300000000 --train_for_env_steps=10000000000 --hidden_size=16 --neighbor_obs_type=none --quads_local_obs=-1 --quads_num_agents=1 --replay_buffer_sample_prob=0.0 --anneal_collision_steps=0 --save_milestones_sec=10000 --quads_neighbor_encoder_type=no_encoder --with_wandb=True --wandb_tags swarm-rl sf1 single --seed=0 --experiment=00_baseline_see_0 --train_dir=./train_dir --experiments_root=quad_single_baseline/baseline_",
+  "cli_args": {
+    "algo": "APPO",
+    "env": "quadrotor_multi",
+    "experiment": "00_baseline_see_0",
+    "experiments_root": "quad_single_baseline/baseline_",
+    "train_dir": "./train_dir",
+    "seed": 0,
+    "save_milestones_sec": 10000,
+    "learning_rate": 0.0001,
+    "train_for_env_steps": 10000000000,
+    "encoder_custom": "quad_multi_encoder",
+    "hidden_size": 16,
+    "nonlinearity": "tanh",
+    "policy_initialization": "xavier_uniform",
+    "actor_critic_share_weights": false,
+    "adaptive_stddev": false,
+    "gae_lambda": 1.0,
+    "rollout": 128,
+    "num_workers": 36,
+    "recurrence": 1,
+    "use_rnn": false,
+    "ppo_clip_value": 5.0,
+    "batch_size": 1024,
+    "max_grad_norm": 5.0,
+    "exploration_loss_coeff": 0.0,
+    "num_envs_per_worker": 4,
+    "max_policy_lag": 100000000,
+    "with_vtrace": false,
+    "with_pbt": false,
+    "with_wandb": true,
+    "wandb_tags": [
+      "swarm-rl",
+      "sf1",
+      "single"
+    ],
+    "quads_episode_duration": 15.0,
+    "quads_num_agents": 1,
+    "quads_neighbor_hidden_size": 256,
+    "quads_neighbor_encoder_type": "no_encoder",
+    "quads_collision_reward": 5.0,
+    "quads_settle_reward": 0.0,
+    "quads_collision_hitbox_radius": 2.0,
+    "quads_collision_falloff_radius": 4.0,
+    "quads_collision_smooth_max_penalty": 10.0,
+    "neighbor_obs_type": "none",
+    "quads_use_numba": true,
+    "quads_local_obs": -1,
+    "quads_local_coeff": 1.0,
+    "quads_local_metric": "dist",
+    "quads_mode": "mix",
+    "quads_formation_size": 0.0,
+    "replay_buffer_sample_prob": 0.0,
+    "anneal_collision_steps": 0.0
+  },
+  "git_hash": "7f0b112fb802e279ad83b0391d962b665dc44ba1",
+  "git_repo_name": "https://github.com/Zhehui-Huang/quad-swarm-rl.git",
+  "wandb_unique_id": "00_baseline_see_0_20220709_000003_519217"
+}

checkpoint_p0/checkpoint_001268618_1299064832.pth

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+size 44812

checkpoint_p0/checkpoint_001269222_1299683328.pth

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checkpoint_p0/checkpoint_001269227_1299688448.pth

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git.diff

@@ -0,0 +1,37 @@
+diff --git a/gym_art/quadrotor_multi/quad_scenarios.py b/gym_art/quadrotor_multi/quad_scenarios.py
+index 9f9eb59..b955f9e 100644
+--- a/gym_art/quadrotor_multi/quad_scenarios.py
++++ b/gym_art/quadrotor_multi/quad_scenarios.py
+@@ -4,7 +4,7 @@ import copy
+ 
+ from gym_art.quadrotor_multi.quad_scenarios_utils import QUADS_PARAMS_DICT, update_formation_and_max_agent_per_layer, \
+     update_layer_dist, get_formation_range, get_goal_by_formation, get_z_value, QUADS_MODE_LIST, \
+-    QUADS_MODE_LIST_OBSTACLES
++    QUADS_MODE_LIST_OBSTACLES, QUADS_MODE_LIST_SIMPLE
+ from gym_art.quadrotor_multi.quad_utils import generate_points, get_grid_dim_number
+ 
+ 
+@@ -623,7 +623,9 @@ class Scenario_mix(QuadrotorScenario):
+         # Once change the parameter here, should also update QUADS_PARAMS_DICT to make sure it is same as run a single scenario
+         # key: quads_mode
+         # value: 0. formation, 1: [formation_low_size, formation_high_size], 2: episode_time
+-        if obst_mode == 'no_obstacles':
++        if num_agents == 1:
++            self.quads_mode_list = QUADS_MODE_LIST_SIMPLE
++        elif obst_mode == 'no_obstacles':
+             self.quads_mode_list = QUADS_MODE_LIST
+         else:
+             self.quads_mode_list = QUADS_MODE_LIST_OBSTACLES
+diff --git a/gym_art/quadrotor_multi/quad_utils.py b/gym_art/quadrotor_multi/quad_utils.py
+index d56c5d5..a8792fc 100644
+--- a/gym_art/quadrotor_multi/quad_utils.py
++++ b/gym_art/quadrotor_multi/quad_utils.py
+@@ -208,7 +208,7 @@ def points_in_sphere(n, x):
+ 
+ def generate_points(n=3):
+     if n < 3:
+-        print("The number of goals can not smaller than 3, The system has cast it to 3")
++        # print("The number of goals can not smaller than 3, The system has cast it to 3")
+         n = 3
+     return points_in_sphere(n, 0.1 + 1.2 * n)
+ 

sf_log.txt

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+size 26137465