reinforcex 0.0.3

Deep Reinforcement Learning Framework
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

use super::base_policy_network::BasePolicy;

use crate::misc::weight_initializer::he_init;
use crate::prob_distributions::BaseDistribution;
use crate::prob_distributions::SoftmaxDistribution;
use tch::nn::{Init, Linear, LinearConfig, Module};
use tch::{nn, Tensor};

pub struct FCSoftmaxPolicy {
    layers: Vec<Linear>,
    logits_layer: Linear,
    n_input_channels: i64,
    n_actions: i64,
    min_prob: f64,
}

pub struct FCSoftmaxPolicyWithValue {
    base_policy: FCSoftmaxPolicy,
    value_layer: Linear,
}

impl FCSoftmaxPolicy {
    pub fn new(
        vs: &nn::VarStore,
        n_input_channels: i64,
        n_actions: i64,
        n_hidden_layers: usize,
        n_hidden_channels: Option<i64>,
        min_prob: f64,
    ) -> Self {
        let root = vs.root();
        let mut layers = Vec::new();
        let n_hidden_channels = n_hidden_channels.unwrap_or(256);

        layers.push(nn::linear(
            &root,
            n_input_channels,
            n_hidden_channels,
            LinearConfig {
                ws_init: he_init(n_input_channels),
                bs_init: Some(Init::Const(0.0)),
                bias: true,
            },
        ));
        for _ in 0..n_hidden_layers {
            layers.push(nn::linear(
                &root,
                n_hidden_channels,
                n_hidden_channels,
                LinearConfig {
                    ws_init: he_init(n_hidden_channels),
                    bs_init: Some(Init::Const(0.0)),
                    bias: true,
                },
            ));
        }

        let logits_layer: Linear = nn::linear(
            &root,
            n_hidden_channels,
            n_actions,
            LinearConfig {
                ws_init: he_init(n_hidden_channels),
                bs_init: Some(Init::Const(0.0)),
                bias: true,
            },
        );

        FCSoftmaxPolicy {
            layers,
            logits_layer,
            n_input_channels,
            n_actions,
            min_prob,
        }
    }

    fn compute_medium_layer(&self, x: &Tensor) -> Tensor {
        let mut h = x.view([-1, self.n_input_channels]);

        for layer in &self.layers {
            h = (layer.forward(&h)).relu();
        }

        h
    }
}

impl BasePolicy for FCSoftmaxPolicy {
    fn forward(&self, x: &Tensor) -> (Box<dyn BaseDistribution>, Option<Tensor>) {
        let h = self.compute_medium_layer(x);
        let logits = self.logits_layer.forward(&h).relu();
        (
            Box::new(SoftmaxDistribution::new(logits, 1.0, self.min_prob)),
            None,
        )
    }

    fn is_cuda(&self) -> bool {
        false
    }
}

impl FCSoftmaxPolicyWithValue {
    pub fn new(
        vs: &nn::VarStore,
        n_input_channels: i64,
        n_actions: i64,
        n_hidden_layers: usize,
        n_hidden_channels: Option<i64>,
        min_prob: f64,
    ) -> Self {
        let base_policy: FCSoftmaxPolicy = FCSoftmaxPolicy::new(
            vs,
            n_input_channels,
            n_actions,
            n_hidden_layers,
            n_hidden_channels,
            min_prob,
        );

        let root = vs.root();
        let n_hidden_channels = n_hidden_channels.unwrap_or(256);
        let value_layer = nn::linear(
            &root,
            n_hidden_channels,
            1,
            LinearConfig {
                ws_init: he_init(n_hidden_channels),
                bs_init: Some(Init::Const(0.0)),
                bias: true,
            },
        );

        FCSoftmaxPolicyWithValue {
            base_policy,
            value_layer,
        }
    }
}

impl BasePolicy for FCSoftmaxPolicyWithValue {
    fn forward(&self, x: &Tensor) -> (Box<dyn BaseDistribution>, Option<Tensor>) {
        let h = self.base_policy.compute_medium_layer(x);
        let logits = self.base_policy.logits_layer.forward(&h).relu();
        let value = self.value_layer.forward(&h);
        (
            Box::new(SoftmaxDistribution::new(
                logits,
                1.0,
                self.base_policy.min_prob,
            )),
            Some(value),
        )
    }

    fn is_cuda(&self) -> bool {
        self.base_policy.is_cuda()
    }
}