tianshou.policy¶

class tianshou.policy.A2CPolicy(actor: torch.nn.modules.module.Module, critic: torch.nn.modules.module.Module, optim: torch.optim.optimizer.Optimizer, dist_fn: torch.distributions.distribution.Distribution = <class 'torch.distributions.categorical.Categorical'>, discount_factor: float = 0.99, vf_coef: float = 0.5, ent_coef: float = 0.01, max_grad_norm: Optional[float] = None, gae_lambda: float = 0.95, reward_normalization: bool = False, **kwargs)[source]¶

Bases: tianshou.policy.modelfree.pg.PGPolicy

Implementation of Synchronous Advantage Actor-Critic. arXiv:1602.01783

Parameters

actor (torch.nn.Module) – the actor network following the rules in BasePolicy. (s -> logits)
critic (torch.nn.Module) – the critic network. (s -> V(s))
optim (torch.optim.Optimizer) – the optimizer for actor and critic network.
dist_fn (torch.distributions.Distribution) – for computing the action, defaults to torch.distributions.Categorical.
discount_factor (float) – in [0, 1], defaults to 0.99.
vf_coef (float) – weight for value loss, defaults to 0.5.
ent_coef (float) – weight for entropy loss, defaults to 0.01.
max_grad_norm (float) – clipping gradients in back propagation, defaults to None.
gae_lambda (float) – in [0, 1], param for Generalized Advantage Estimation, defaults to 0.95.

See also

Please refer to BasePolicy for more detailed explanation.

forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Returns

A Batch which has 4 keys:

act the action.
logits the network’s raw output.
dist the action distribution.
state the hidden state.

See also

Please refer to forward() for more detailed explanation.

learn(batch: tianshou.data.batch.Batch, batch_size: int, repeat: int, **kwargs) → Dict[str, List[float]][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

process_fn(batch: tianshou.data.batch.Batch, buffer: tianshou.data.buffer.ReplayBuffer, indice: numpy.ndarray) → tianshou.data.batch.Batch[source]¶

Compute the discounted returns for each frame:

$G_t = \sum_{i=t}^T \gamma^{i-t}r_i$

, where $T$ is the terminal time step, $\gamma$ is the discount factor, $\gamma \in [0, 1]$ .

class tianshou.policy.BasePolicy(**kwargs)[source]¶

Bases: abc.ABC, torch.nn.modules.module.Module

Tianshou aims to modularizing RL algorithms. It comes into several classes of policies in Tianshou. All of the policy classes must inherit BasePolicy.

A policy class typically has four parts:

__init__(): initialize the policy, including coping the target network and so on;
forward(): compute action with given observation;
process_fn(): pre-process data from the replay buffer (this function can interact with replay buffer);
learn(): update policy with a given batch of data.

Most of the policy needs a neural network to predict the action and an optimizer to optimize the policy. The rules of self-defined networks are:

Input: observation obs (may be a numpy.ndarray, a torch.Tensor, a dict or any others), hidden state state (for RNN usage), and other information info provided by the environment.
Output: some logits, the next hidden state state, and the intermediate result during policy forwarding procedure policy. The logits could be a tuple instead of a torch.Tensor. It depends on how the policy process the network output. For example, in PPO, the return of the network might be (mu, sigma), state for Gaussian policy. The policy can be a Batch of torch.Tensor or other things, which will be stored in the replay buffer, and can be accessed in the policy update process (e.g. in policy.learn(), the batch.policy is what you need).

Since BasePolicy inherits torch.nn.Module, you can use BasePolicy almost the same as torch.nn.Module, for instance, loading and saving the model:

torch.save(policy.state_dict(), 'policy.pth')
policy.load_state_dict(torch.load('policy.pth'))

static compute_episodic_return(batch: tianshou.data.batch.Batch, v_s_: Optional[Union[numpy.ndarray, torch.Tensor]] = None, gamma: float = 0.99, gae_lambda: float = 0.95) → tianshou.data.batch.Batch[source]¶

Compute returns over given full-length episodes, including the implementation of Generalized Advantage Estimator (arXiv:1506.02438).

Parameters

batch (Batch) – a data batch which contains several full-episode data chronologically.
v_s (numpy.ndarray) – the value function of all next states $V(s')$ .
gamma (float) – the discount factor, should be in [0, 1], defaults to 0.99.
gae_lambda (float) – the parameter for Generalized Advantage Estimation, should be in [0, 1], defaults to 0.95.

Returns

a Batch. The result will be stored in batch.returns.

static compute_nstep_return(batch: tianshou.data.batch.Batch, buffer: tianshou.data.buffer.ReplayBuffer, indice: numpy.ndarray, target_q_fn: Callable[[tianshou.data.buffer.ReplayBuffer, numpy.ndarray], torch.Tensor], gamma: float = 0.99, n_step: int = 1, rew_norm: bool = False) → numpy.ndarray[source]¶

Compute n-step return for Q-learning targets:

$G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i + \gamma^n (1 - d_{t + n}) Q_{\mathrm{target}}(s_{t + n})$

, where $\gamma$ is the discount factor, $\gamma \in [0, 1]$ , $d_t$ is the done flag of step $t$ .

Parameters

batch (Batch) – a data batch, which is equal to buffer[indice].
buffer (ReplayBuffer) – a data buffer which contains several full-episode data chronologically.
indice (numpy.ndarray) – sampled timestep.
target_q_fn (function) – a function receives $t+n-1$ step’s data and compute target Q value.
gamma (float) – the discount factor, should be in [0, 1], defaults to 0.99.
n_step (int) – the number of estimation step, should be an int greater than 0, defaults to 1.
rew_norm (bool) – normalize the reward to Normal(0, 1), defaults to False.

Returns

a Batch. The result will be stored in batch.returns as a torch.Tensor with shape (bsz, ).

abstract forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Returns

A Batch which MUST have the following keys:

act an numpy.ndarray or a torch.Tensor, the action over given batch data.
state a dict, an numpy.ndarray or a torch.Tensor, the internal state of the policy, None as default.

Other keys are user-defined. It depends on the algorithm. For example,

# some code
return Batch(logits=..., act=..., state=None, dist=...)

After version >= 0.2.3, the keyword “policy” is reserverd and the corresponding data will be stored into the replay buffer in numpy. For instance,

# some code
return Batch(..., policy=Batch(log_prob=dist.log_prob(act)))
# and in the sampled data batch, you can directly call
# batch.policy.log_prob to get your data, although it is stored in
# np.ndarray.

abstract learn(batch: tianshou.data.batch.Batch, **kwargs) → Dict[str, Union[float, List[float]]][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

process_fn(batch: tianshou.data.batch.Batch, buffer: tianshou.data.buffer.ReplayBuffer, indice: numpy.ndarray) → tianshou.data.batch.Batch[source]¶: Pre-process the data from the provided replay buffer. Check out Policy for more information.

class tianshou.policy.DDPGPolicy(actor: torch.nn.modules.module.Module, actor_optim: torch.optim.optimizer.Optimizer, critic: torch.nn.modules.module.Module, critic_optim: torch.optim.optimizer.Optimizer, tau: float = 0.005, gamma: float = 0.99, exploration_noise: Optional[tianshou.exploration.random.BaseNoise] = <tianshou.exploration.random.GaussianNoise object>, action_range: Optional[Tuple[float, float]] = None, reward_normalization: bool = False, ignore_done: bool = False, estimation_step: int = 1, **kwargs)[source]¶

Bases: tianshou.policy.base.BasePolicy

Implementation of Deep Deterministic Policy Gradient. arXiv:1509.02971

Parameters

actor (torch.nn.Module) – the actor network following the rules in BasePolicy. (s -> logits)
actor_optim (torch.optim.Optimizer) – the optimizer for actor network.
critic (torch.nn.Module) – the critic network. (s, a -> Q(s, a))
critic_optim (torch.optim.Optimizer) – the optimizer for critic network.
tau (float) – param for soft update of the target network, defaults to 0.005.
gamma (float) – discount factor, in [0, 1], defaults to 0.99.
exploration_noise (BaseNoise) – the exploration noise, add to the action, defaults to GaussianNoise(sigma=0.1).
action_range ((float, float)) – the action range (minimum, maximum).
reward_normalization (bool) – normalize the reward to Normal(0, 1), defaults to False.
ignore_done (bool) – ignore the done flag while training the policy, defaults to False.
estimation_step (int) – greater than 1, the number of steps to look ahead.

See also

Please refer to BasePolicy for more detailed explanation.

forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, model: str = 'actor', input: str = 'obs', explorating: bool = True, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Returns

A Batch which has 2 keys:

act the action.
state the hidden state.

See also

Please refer to forward() for more detailed explanation.

learn(batch: tianshou.data.batch.Batch, **kwargs) → Dict[str, float][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

process_fn(batch: tianshou.data.batch.Batch, buffer: tianshou.data.buffer.ReplayBuffer, indice: numpy.ndarray) → tianshou.data.batch.Batch[source]¶: Pre-process the data from the provided replay buffer. Check out Policy for more information.

set_exp_noise(noise: Optional[tianshou.exploration.random.BaseNoise]) → None[source]¶: Set the exploration noise.

sync_weight() → None[source]¶: Soft-update the weight for the target network.

train(mode=True) → torch.nn.modules.module.Module[source]¶: Set the module in training mode, except for the target network.

class tianshou.policy.DQNPolicy(model: torch.nn.modules.module.Module, optim: torch.optim.optimizer.Optimizer, discount_factor: float = 0.99, estimation_step: int = 1, target_update_freq: Optional[int] = 0, **kwargs)[source]¶

Bases: tianshou.policy.base.BasePolicy

Implementation of Deep Q Network. arXiv:1312.5602 Implementation of Double Q-Learning. arXiv:1509.06461

Parameters

model (torch.nn.Module) – a model following the rules in BasePolicy. (s -> logits)
optim (torch.optim.Optimizer) – a torch.optim for optimizing the model.
discount_factor (float) – in [0, 1].
estimation_step (int) – greater than 1, the number of steps to look ahead.
target_update_freq (int) – the target network update frequency (0 if you do not use the target network).

See also

Please refer to BasePolicy for more detailed explanation.

forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, model: str = 'model', input: str = 'obs', eps: Optional[float] = None, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Parameters

eps (float) – in [0, 1], for epsilon-greedy exploration method.

Returns

A Batch which has 3 keys:

act the action.
logits the network’s raw output.
state the hidden state.

See also

Please refer to forward() for more detailed explanation.

learn(batch: tianshou.data.batch.Batch, **kwargs) → Dict[str, float][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

process_fn(batch: tianshou.data.batch.Batch, buffer: tianshou.data.buffer.ReplayBuffer, indice: numpy.ndarray) → tianshou.data.batch.Batch[source]¶

Compute the n-step return for Q-learning targets:

$G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i + \gamma^n (1 - d_{t + n}) \max_a Q_{old}(s_{t + n}, \arg\max_a (Q_{new}(s_{t + n}, a)))$

, where $\gamma$ is the discount factor, $\gamma \in [0, 1]$ , $d_t$ is the done flag of step $t$ . If there is no target network, the $Q_{old}$ is equal to $Q_{new}$ .

set_eps(eps: float) → None[source]¶: Set the eps for epsilon-greedy exploration.

sync_weight() → None[source]¶: Synchronize the weight for the target network.

train(mode=True) → torch.nn.modules.module.Module[source]¶: Set the module in training mode, except for the target network.

class tianshou.policy.ImitationPolicy(model: torch.nn.modules.module.Module, optim: torch.optim.optimizer.Optimizer, mode: str = 'continuous', **kwargs)[source]¶

Bases: tianshou.policy.base.BasePolicy

Implementation of vanilla imitation learning (for continuous action space).

Parameters

model (torch.nn.Module) – a model following the rules in BasePolicy. (s -> a)
optim (torch.optim.Optimizer) – for optimizing the model.
mode (str) – indicate the imitation type (“continuous” or “discrete” action space), defaults to “continuous”.

See also

Please refer to BasePolicy for more detailed explanation.

forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Returns

A Batch which MUST have the following keys:

act an numpy.ndarray or a torch.Tensor, the action over given batch data.
state a dict, an numpy.ndarray or a torch.Tensor, the internal state of the policy, None as default.

Other keys are user-defined. It depends on the algorithm. For example,

# some code
return Batch(logits=..., act=..., state=None, dist=...)

After version >= 0.2.3, the keyword “policy” is reserverd and the corresponding data will be stored into the replay buffer in numpy. For instance,

# some code
return Batch(..., policy=Batch(log_prob=dist.log_prob(act)))
# and in the sampled data batch, you can directly call
# batch.policy.log_prob to get your data, although it is stored in
# np.ndarray.

learn(batch: tianshou.data.batch.Batch, **kwargs) → Dict[str, float][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

class tianshou.policy.PGPolicy(model: torch.nn.modules.module.Module, optim: torch.optim.optimizer.Optimizer, dist_fn: torch.distributions.distribution.Distribution = <class 'torch.distributions.categorical.Categorical'>, discount_factor: float = 0.99, reward_normalization: bool = False, **kwargs)[source]¶

Bases: tianshou.policy.base.BasePolicy

Implementation of Vanilla Policy Gradient.

Parameters

model (torch.nn.Module) – a model following the rules in BasePolicy. (s -> logits)
optim (torch.optim.Optimizer) – a torch.optim for optimizing the model.
dist_fn (torch.distributions.Distribution) – for computing the action.
discount_factor (float) – in [0, 1].

See also

Please refer to BasePolicy for more detailed explanation.

forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Returns

A Batch which has 4 keys:

act the action.
logits the network’s raw output.
dist the action distribution.
state the hidden state.

See also

Please refer to forward() for more detailed explanation.

learn(batch: tianshou.data.batch.Batch, batch_size: int, repeat: int, **kwargs) → Dict[str, List[float]][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

process_fn(batch: tianshou.data.batch.Batch, buffer: tianshou.data.buffer.ReplayBuffer, indice: numpy.ndarray) → tianshou.data.batch.Batch[source]¶

Compute the discounted returns for each frame:

$G_t = \sum_{i=t}^T \gamma^{i-t}r_i$

, where $T$ is the terminal time step, $\gamma$ is the discount factor, $\gamma \in [0, 1]$ .

class tianshou.policy.PPOPolicy(actor: torch.nn.modules.module.Module, critic: torch.nn.modules.module.Module, optim: torch.optim.optimizer.Optimizer, dist_fn: torch.distributions.distribution.Distribution, discount_factor: float = 0.99, max_grad_norm: Optional[float] = None, eps_clip: float = 0.2, vf_coef: float = 0.5, ent_coef: float = 0.01, action_range: Optional[Tuple[float, float]] = None, gae_lambda: float = 0.95, dual_clip: Optional[float] = None, value_clip: bool = True, reward_normalization: bool = True, **kwargs)[source]¶

Bases: tianshou.policy.modelfree.pg.PGPolicy

Implementation of Proximal Policy Optimization. arXiv:1707.06347

Parameters

actor (torch.nn.Module) – the actor network following the rules in BasePolicy. (s -> logits)
critic (torch.nn.Module) – the critic network. (s -> V(s))
optim (torch.optim.Optimizer) – the optimizer for actor and critic network.
dist_fn (torch.distributions.Distribution) – for computing the action.
discount_factor (float) – in [0, 1], defaults to 0.99.
max_grad_norm (float) – clipping gradients in back propagation, defaults to None.
eps_clip (float) – $\epsilon$ in $L_{CLIP}$ in the original paper, defaults to 0.2.
vf_coef (float) – weight for value loss, defaults to 0.5.
ent_coef (float) – weight for entropy loss, defaults to 0.01.
action_range ((float, float)) – the action range (minimum, maximum).
gae_lambda (float) – in [0, 1], param for Generalized Advantage Estimation, defaults to 0.95.
dual_clip (float) – a parameter c mentioned in arXiv:1912.09729 Equ. 5, where c > 1 is a constant indicating the lower bound, defaults to 5.0 (set None if you do not want to use it).
value_clip (bool) – a parameter mentioned in arXiv:1811.02553 Sec. 4.1, defaults to True.
reward_normalization (bool) – normalize the returns to Normal(0, 1), defaults to True.

See also

Please refer to BasePolicy for more detailed explanation.

forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Returns

A Batch which has 4 keys:

act the action.
logits the network’s raw output.
dist the action distribution.
state the hidden state.

See also

Please refer to forward() for more detailed explanation.

learn(batch: tianshou.data.batch.Batch, batch_size: int, repeat: int, **kwargs) → Dict[str, List[float]][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

process_fn(batch: tianshou.data.batch.Batch, buffer: tianshou.data.buffer.ReplayBuffer, indice: numpy.ndarray) → tianshou.data.batch.Batch[source]¶

Compute the discounted returns for each frame:

$G_t = \sum_{i=t}^T \gamma^{i-t}r_i$

, where $T$ is the terminal time step, $\gamma$ is the discount factor, $\gamma \in [0, 1]$ .

class tianshou.policy.SACPolicy(actor: torch.nn.modules.module.Module, actor_optim: torch.optim.optimizer.Optimizer, critic1: torch.nn.modules.module.Module, critic1_optim: torch.optim.optimizer.Optimizer, critic2: torch.nn.modules.module.Module, critic2_optim: torch.optim.optimizer.Optimizer, tau: float = 0.005, gamma: float = 0.99, alpha: Tuple[float, torch.Tensor, torch.optim.optimizer.Optimizer] = 0.2, action_range: Optional[Tuple[float, float]] = None, reward_normalization: bool = False, ignore_done: bool = False, estimation_step: int = 1, exploration_noise: Optional[tianshou.exploration.random.BaseNoise] = None, **kwargs)[source]¶

Bases: tianshou.policy.modelfree.ddpg.DDPGPolicy

Implementation of Soft Actor-Critic. arXiv:1812.05905

Parameters

actor (torch.nn.Module) – the actor network following the rules in BasePolicy. (s -> logits)
actor_optim (torch.optim.Optimizer) – the optimizer for actor network.
critic1 (torch.nn.Module) – the first critic network. (s, a -> Q(s, a))
critic1_optim (torch.optim.Optimizer) – the optimizer for the first critic network.
critic2 (torch.nn.Module) – the second critic network. (s, a -> Q(s, a))
critic2_optim (torch.optim.Optimizer) – the optimizer for the second critic network.
tau (float) – param for soft update of the target network, defaults to 0.005.
gamma (float) – discount factor, in [0, 1], defaults to 0.99.
exploration_noise (BaseNoise) – the noise intensity, add to the action, defaults to 0.1.
torch.Tensor, torch.optim.Optimizer) or float alpha ((float,) – entropy regularization coefficient, default to 0.2. If a tuple (target_entropy, log_alpha, alpha_optim) is provided, then alpha is automatatically tuned.
action_range ((float, float)) – the action range (minimum, maximum).
reward_normalization (bool) – normalize the reward to Normal(0, 1), defaults to False.
ignore_done (bool) – ignore the done flag while training the policy, defaults to False.
exploration_noise – add a noise to action for exploration. This is useful when solving hard-exploration problem.

See also

Please refer to BasePolicy for more detailed explanation.

forward(batch: tianshou.data.batch.Batch, state: Optional[Union[dict, tianshou.data.batch.Batch, numpy.ndarray]] = None, input: str = 'obs', explorating: bool = True, **kwargs) → tianshou.data.batch.Batch[source]¶

Compute action over the given batch data.

Returns

A Batch which has 2 keys:

act the action.
state the hidden state.

See also

Please refer to forward() for more detailed explanation.

learn(batch: tianshou.data.batch.Batch, **kwargs) → Dict[str, float][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

sync_weight() → None[source]¶: Soft-update the weight for the target network.

train(mode=True) → torch.nn.modules.module.Module[source]¶: Set the module in training mode, except for the target network.

class tianshou.policy.TD3Policy(actor: torch.nn.modules.module.Module, actor_optim: torch.optim.optimizer.Optimizer, critic1: torch.nn.modules.module.Module, critic1_optim: torch.optim.optimizer.Optimizer, critic2: torch.nn.modules.module.Module, critic2_optim: torch.optim.optimizer.Optimizer, tau: float = 0.005, gamma: float = 0.99, exploration_noise: Optional[tianshou.exploration.random.BaseNoise] = <tianshou.exploration.random.GaussianNoise object>, policy_noise: float = 0.2, update_actor_freq: int = 2, noise_clip: float = 0.5, action_range: Optional[Tuple[float, float]] = None, reward_normalization: bool = False, ignore_done: bool = False, estimation_step: int = 1, **kwargs)[source]¶

Bases: tianshou.policy.modelfree.ddpg.DDPGPolicy

Implementation of Twin Delayed Deep Deterministic Policy Gradient, arXiv:1802.09477

Parameters

actor (torch.nn.Module) – the actor network following the rules in BasePolicy. (s -> logits)
actor_optim (torch.optim.Optimizer) – the optimizer for actor network.
critic1 (torch.nn.Module) – the first critic network. (s, a -> Q(s, a))
critic1_optim (torch.optim.Optimizer) – the optimizer for the first critic network.
critic2 (torch.nn.Module) – the second critic network. (s, a -> Q(s, a))
critic2_optim (torch.optim.Optimizer) – the optimizer for the second critic network.
tau (float) – param for soft update of the target network, defaults to 0.005.
gamma (float) – discount factor, in [0, 1], defaults to 0.99.
exploration_noise (float) – the exploration noise, add to the action, defaults to GaussianNoise(sigma=0.1)
policy_noise (float) – the noise used in updating policy network, default to 0.2.
update_actor_freq (int) – the update frequency of actor network, default to 2.
noise_clip (float) – the clipping range used in updating policy network, default to 0.5.
action_range ((float, float)) – the action range (minimum, maximum).
reward_normalization (bool) – normalize the reward to Normal(0, 1), defaults to False.
ignore_done (bool) – ignore the done flag while training the policy, defaults to False.

See also

Please refer to BasePolicy for more detailed explanation.

learn(batch: tianshou.data.batch.Batch, **kwargs) → Dict[str, float][source]¶

Update policy with a given batch of data.

Returns: A dict which includes loss and its corresponding label.

sync_weight() → None[source]¶: Soft-update the weight for the target network.

train(mode=True) → torch.nn.modules.module.Module[source]¶: Set the module in training mode, except for the target network.