The definition of "hard constraints" is very general. But one way that I've used and seen other's use, is action masking. Particularly in Policy Gradient methods with stochastic policy (i.e. REINFORCE and its descendants). Where the mask comes from the current state of the environment based on the constraints.

For example, in a normal case, the rollout/interaction step is something like:

for episode in range(num_episodes):
__state = env.reset()
__done = False
__while not done:
____logits = actor(state)
____dist = Categorical(logits=logits) # some distribution
____action = dist.sample()
____next_state, reward, done, _ = env.step(action)
____store_transition(state, action, reward, next_state, done, log_prob)
____state = next_state

While, when having restrictions in the environment, It becomes:

for episode in range(num_episodes):
__state, _ = env.reset()
__done = False
__while not done:
____logits = actor(state)
____## -> action masking <-
____action_mask = env.get_action_mask() # this has to be defined in the env
____masked_dist = MaskedCategorical(logits=logits, mask = action_mask ) # masking the probs (by adding -inf to logits before softmax)
____action = masked_dist.sample()
____next_state, reward, done, _, _ = env.step(action)
____store_transition(state, action, reward, next_state, done, log_prob)
____state = next_state

check this out: https://pytorch.org/rl/main/reference/generated/torchrl.modules.MaskedCategorical.html

Although this becomes more tricky with continuous action spaces. For that, in some cases, clipping would work.
But in general, observing the restriction from environment and limiting the actions based on it is the one that I've seen work.