Hugging Face's logo

sgoodfriend
/
a2c-HalfCheetahBulletEnv-v0

Reinforcement Learning
rl-algo-impls
HalfCheetahBulletEnv-v0
a2c
deep-reinforcement-learning
Eval Results
Model card Files Community
a2c-HalfCheetahBulletEnv-v0
File size: 4,006 Bytes 
3d6ce6f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
 
CartPole-v1: &cartpole-defaults
  n_timesteps: !!float 4e5
  algo_hyperparams:
    n_steps: 4096
    pi_lr: 0.01
    gamma: 0.99
    gae_lambda: 1
    val_lr: 0.01
    train_v_iters: 80
  eval_params:
    step_freq: !!float 2.5e4

CartPole-v0:
  <<: *cartpole-defaults
  n_timesteps: !!float 1e5
  algo_hyperparams:
    n_steps: 1024
    pi_lr: 0.01
    gamma: 0.99
    gae_lambda: 1
    val_lr: 0.01
    train_v_iters: 80

MountainCar-v0:
  n_timesteps: !!float 1e6
  env_hyperparams:
    normalize: true
    n_envs: 16
  algo_hyperparams:
    n_steps: 200
    pi_lr: 0.005
    gamma: 0.99
    gae_lambda: 0.97
    val_lr: 0.01
    train_v_iters: 80
    max_grad_norm: 0.5

MountainCarContinuous-v0:
  n_timesteps: !!float 3e5
  env_hyperparams:
    normalize: true
    n_envs: 4
  # policy_hyperparams:
  #   init_layers_orthogonal: false
  #   log_std_init: -3.29
  #   use_sde: true
  algo_hyperparams:
    n_steps: 1000
    pi_lr: !!float 5e-4
    gamma: 0.99
    gae_lambda: 0.9
    val_lr: !!float 1e-3
    train_v_iters: 80
    max_grad_norm: 5
  eval_params:
    step_freq: 5000

Acrobot-v1:
  n_timesteps: !!float 2e5
  algo_hyperparams:
    n_steps: 2048
    pi_lr: 0.005
    gamma: 0.99
    gae_lambda: 0.97
    val_lr: 0.01
    train_v_iters: 80
    max_grad_norm: 0.5

LunarLander-v2:
  n_timesteps: !!float 4e6
  policy_hyperparams:
    hidden_sizes: [256, 256]
  algo_hyperparams:
    n_steps: 2048
    pi_lr: 0.0001
    gamma: 0.999
    gae_lambda: 0.97
    val_lr: 0.0001
    train_v_iters: 80
    max_grad_norm: 0.5
  eval_params:
    deterministic: false

BipedalWalker-v3:
  n_timesteps: !!float 10e6
  env_hyperparams:
    n_envs: 16
    normalize: true
  policy_hyperparams:
    hidden_sizes: [256, 256]
  algo_hyperparams:
    n_steps: 1600
    gae_lambda: 0.95
    gamma: 0.99
    pi_lr: !!float 1e-4
    val_lr: !!float 1e-4
    train_v_iters: 80
    max_grad_norm: 0.5
  eval_params:
    deterministic: false

CarRacing-v0:
  n_timesteps: !!float 4e6
  env_hyperparams:
    frame_stack: 4
    n_envs: 4
    vec_env_class: sync
  policy_hyperparams:
    use_sde: true
    log_std_init: -2
    init_layers_orthogonal: false
    activation_fn: relu
    cnn_feature_dim: 256
    hidden_sizes: [256]
  algo_hyperparams:
    n_steps: 1000
    pi_lr: !!float 5e-5
    gamma: 0.99
    gae_lambda: 0.95
    val_lr: !!float 1e-4
    train_v_iters: 40
    max_grad_norm: 0.5
    sde_sample_freq: 4

HalfCheetahBulletEnv-v0: &pybullet-defaults
  n_timesteps: !!float 2e6
  env_hyperparams: &pybullet-env-defaults
    normalize: true
  policy_hyperparams: &pybullet-policy-defaults
    hidden_sizes: [256, 256]
  algo_hyperparams: &pybullet-algo-defaults
    n_steps: 4000
    pi_lr: !!float 3e-4
    gamma: 0.99
    gae_lambda: 0.97
    val_lr: !!float 1e-3
    train_v_iters: 80
    max_grad_norm: 0.5

AntBulletEnv-v0:
  <<: *pybullet-defaults
  policy_hyperparams:
    <<: *pybullet-policy-defaults
    hidden_sizes: [400, 300]
  algo_hyperparams:
    <<: *pybullet-algo-defaults
    pi_lr: !!float 7e-4
    val_lr: !!float 7e-3

HopperBulletEnv-v0:
  <<: *pybullet-defaults

Walker2DBulletEnv-v0:
  <<: *pybullet-defaults

FrozenLake-v1:
  n_timesteps: !!float 8e5
  env_params:
    make_kwargs:
      map_name: 8x8
      is_slippery: true
  policy_hyperparams:
    hidden_sizes: [64]
  algo_hyperparams:
    n_steps: 2048
    pi_lr: 0.01
    gamma: 0.99
    gae_lambda: 0.98
    val_lr: 0.01
    train_v_iters: 80
    max_grad_norm: 0.5
  eval_params:
    step_freq: !!float 5e4
    n_episodes: 10
    save_best: true

_atari: &atari-defaults
  n_timesteps: !!float 25e6
  env_hyperparams:
    n_envs: 4
    frame_stack: 4
    no_reward_timeout_steps: 1000
    no_reward_fire_steps: 500
    vec_env_class: async
  policy_hyperparams:
    activation_fn: relu
  algo_hyperparams:
    n_steps: 2048
    pi_lr: !!float 5e-5
    gamma: 0.99
    gae_lambda: 0.95
    val_lr: !!float 1e-4
    train_v_iters: 80
    max_grad_norm: 0.5
    ent_coef: 0.01
  eval_params:
    deterministic: false