# Reinforcement Learning - The Actor-Critic Algorithm

This chapter is from the book

## 6.6 Training an A2C Agent

In this section we show how to train an Actor-Critic agent to play Atari Pong using different advantage estimates—first n-step returns, then GAE. Then, we apply A2C with GAE to a continuous-control environment BipedalWalker.

### 6.6.1 A2C with n-Step Returns on Pong

A spec file which configures an Actor-Critic agent with n-step returns advantage estimate is shown in Code 6.7. The file is available in SLM Lab at slm_lab/spec/benchmark/a2c/a2c_nstep_pong.json.

#### Code 6.7 A2C with n-step returns: spec file

``` 1  # slm_lab/spec/benchmark/a2c/a2c_nstep_pong.json
2
3   {
4     "a2c_nstep_pong": {
5       "agent": [{
6         "name": "A2C",
7         "algorithm": {
8         "name": "ActorCritic",
9         "action_pdtype": "default",
10         "action_policy": "default",
11         "explore_var_spec": null,
12         "gamma": 0.99,
13         "lam": null,
14         "num_step_returns": 11,
15         "entropy_coef_spec": {
16           "name": "no_decay",
17           "start_val": 0.01,
18           "end_val": 0.01,
19           "start_step": 0,
20           "end_step": 0
21         },
22         "val_loss_coef": 0.5,
23         "training_frequency": 5
24       },
25       "memory": {
26         "name": "OnPolicyBatchReplay"
27       },
28       "net": {
29         "type": "ConvNet",
30         "shared": true,
31         "conv_hid_layers": [
32           [32, 8, 4, 0, 1],
33           [64, 4, 2, 0, 1],
34           [32, 3, 1, 0, 1]
35         ],
36         "fc_hid_layers": [512],
37         "hid_layers_activation": "relu",
38         "init_fn": "orthogonal_",
39         "normalize": true,
40         "batch_norm": false,
42         "use_same_optim": false,
43         "loss_spec": {
44           "name": "MSELoss"
45         },
46         "actor_optim_spec": {
47           "name": "RMSprop",
48           "lr": 7e-4,
49           "alpha": 0.99,
50           "eps": 1e-5
51         },
52         "critic_optim_spec": {
53           "name": "RMSprop",
54           "lr": 7e-4,
55           "alpha": 0.99,
56           "eps": 1e-5
57         },
58         "lr_scheduler_spec": null,
59         "gpu": true
60         }
61       }],
62       "env": [{
63         "name": "PongNoFrameskip-v4",
64         "frame_op": "concat",
65         "frame_op_len": 4,
66         "reward_scale": "sign",
67         "num_envs": 16,
68         "max_t": null,
69         "max_frame": 1e7
70       }],
71       "body": {
72         "product": "outer",
73         "num": 1,
74       },
75       "meta": {
76         "distributed": false,
77         "log_frequency": 10000,
78         "eval_frequency": 10000,
79         "max_session": 4,
80         "max_trial": 1
81       }
82     }
83   }```

Let’s walk through the main components.

• Algorithm: The algorithm is Actor-Critic (line 8), the action policy is the default policy (line 10) for discrete action space (categorical distribution). γ is set on line 12. If lam is specified for λ (not null), then GAE is used to estimate the advantages. If num_step_returns is specified instead, then n-step returns is used (lines 13–14). The entropy coefficient and its decay during training is specified in lines 15–21. The value loss coefficient is specified in line 22.

• Network architecture: Convolutional neural network with three convolutional layers and one fully connected layer with ReLU activation function (lines 29–37). The actor and critic use a shared network as specified in line 30. The network is trained on a GPU if available (line 59).

• Optimizer: The optimizer is RMSprop [50] with a learning rate of 0.0007 (lines 46–51). If separate networks are used instead, it is possible to specify a different optimizer setting for the critic network (lines 52–57) by setting use_same_optim to false (line 42). Since the network is shared in this case, this is not used. There is no learning rate decay (line 58).

• Training frequency: Training is batch-wise because we have selected OnPolicyBatchReplay memory (line 26) and the batch size is 5 × 16. This is controlled by the training_frequency (line 23) and the number of parallel environments (line 67). Parallel environments are discussed in Chapter 8.

• Environment: The environment is Atari Pong [14, 18] (line 63).

• Training length: Training consists of 10 million time steps (line 69).

• Evaluation: The agent is evaluated every 10,000 time steps (line 78).

To train this Actor-Critic agent using SLM Lab, run the commands shown in Code 6.8 in a terminal. The agent should start with the score of -21 and achieve close to the maximum score of 21 on average after 2 million frames.

#### Code 6.8 A2C with n-step returns: training an agent

```1  conda activate lab
2   python run_lab.py slm_lab/spec/benchmark/a2c/a2c_nstep_pong.json
↪ a2c_nstep_pong train```

This will run a training Trial with four Sessions to obtain an average result. The trial should take about half a day to complete when running on a GPU. The graph and its moving average are shown in Figure 6.2.

### 6.6.2 A2C with GAE on Pong

Next, to switch from n-step returns to GAE, simply modify the spec from Code 6.7 to specify a value for lam and set num_step_returns to null, as shown in Code 6.9. The file is also available in SLM Lab at slm_lab/spec/benchmark/a2c/a2c_gae_pong.json.

#### Code 6.9 A2C with GAE: spec file

``` 1 # slm_lab/spec/benchmark/a2c/a2c_gae_pong.json
2
3 {
4     "a2c_gae_pong": {
5       "agent": [{
6         "name": "A2C",
7         "algorithm": {
8           ...
9           "lam": 0.95,
10           "num_step_returns": null,
11           ...
12     }
13 }```

Then, run the commands shown in Code 6.10 in a terminal to train an agent.

#### Code 6.10 A2C with GAE: training an agent

```1 conda activate lab
2  python run_lab.py slm_lab/spec/benchmark/a2c/a2c_gae_pong.json a2c_gae_pong
↪ train```

Similarly, this will run a training Trial to produce the graphs shown in Figure 6.3.

### 6.6.3 A2C with n-Step Returns on BipedalWalker

So far, we have been training on discrete environments. Recall that policy-based method can also be applied directly to continuous-control problems. Now we will look at the BipedalWalker environment that was introduced in Section 1.1.

Code 6.11 shows a spec file which configures an A2C with n-step returns agent for the BipedalWalker environment. The file is available in SLM Lab at slm_lab/spec/benchmark/a2c/a2c_nstep_cont.json. In particular, note the changes in network architecture (lines 29–31) and environment (lines 54–57).

#### Code 6.11 A2C with n-step returns on BipedalWalker: spec file

``` 1  # slm_lab/spec/benchmark/a2c/a2c_nstep_cont.json
2
3   {
4     "a2c_nstep_bipedalwalker": {
5       "agent": [{
6         "name": "A2C",
7         "algorithm": {
8           "name": "ActorCritic",
9           "action_pdtype": "default",
10           "action_policy": "default",
11           "explore_var_spec": null,
12           "gamma": 0.99,
13           "lam": null,
14           "num_step_returns": 5,
15           "entropy_coef_spec": {
16            "name": "no_decay",
17            "start_val": 0.01,
18            "end_val": 0.01,
19            "start_step": 0,
20            "end_step": 0
21           },
22           "val_loss_coef": 0.5,
23           "training_frequency": 256
24         },
25         "memory": {
26           "name": "OnPolicyBatchReplay",
27       },
28       "net": {
29         "type": "MLPNet",
30         "shared": false,
31         "hid_layers": [256, 128],
32         "hid_layers_activation": "relu",
33         "init_fn": "orthogonal_",
34         "normalize": true,
35         "batch_norm": false,
37         "use_same_optim": false,
38         "loss_spec": {
39           "name": "MSELoss"
40         },
41         "actor_optim_spec": {
43          "lr": 3e-4,
44         },
45         "critic_optim_spec": {
47          "lr": 3e-4,
48         },
49         "lr_scheduler_spec": null,
50         "gpu": false
51       }
52     }],
53     "env": [{
54       "name": "BipedalWalker-v2",
55       "num_envs": 32,
56       "max_t": null,
57       "max_frame": 4e6
58     }],
59     "body": {
60       "product": "outer",
61       "num": 1
62     },
63     "meta": {
64       "distributed": false,
65       "log_frequency": 10000,
66       "eval_frequency": 10000,
67       "max_session": 4,
68       "max_trial": 1
69     }
70   }
71  }```

Run the commands shown in Code 6.12 in a terminal to train an agent.

#### Code 6.12 A2C with n-step returns on BipedalWalker: training an agent

```1 conda activate lab
2  python run_lab.py slm_lab/spec/benchmark/a2c/a2c_nstep_cont.json
↪ a2c_nstep_bipedalwalker train```

This will run a training Trial to produce the graphs shown in Figure 6.4.

BipedalWalker is a challenging continuous environment that is considered solved when the total reward moving average is above 300. In Figure 6.4, our agent did not achieve this within 4 million frames. We will return to this problem in Chapter 7 with a better attempt.

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